Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/s2589-7500(24)00248-6

PubMed

Publishing type：Research paper (scientific journal)  

Abstract

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan’s contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI’s growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation’s contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan’s healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan’s national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan’s extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI–AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

DOI： 10.1007/s11604-024-01689-y

PubMed

Other URL： https://link.springer.com/article/10.1007/s11604-024-01689-y/fulltext.html

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

PURPOSE: This study examined the correlation between tumor location and prognosis in patients with glioblastoma using magnetic resonance images of various isocitrate dehydrogenase (IDH) wild-type glioblastomas from The Cancer Imaging Archive (TCIA). The relationship between tumor location and prognosis was visualized using voxel-wise Cox regression analysis. METHODS: Participants with IDH wild-type glioblastoma were selected, and their survival and demographic data and tumor characteristics were collected from TCIA datasets. Post-contrast-enhanced T1-weighted imaging, T2-fluid attenuated inversion recovery imaging, and tumor segmentation data were also compiled. Following affine registration of each image and tumor segmentation region of interest to the MNI standard space, a voxel-wise Cox regression analysis was conducted. This analysis determined the association of the presence or absence of the tumor with the prognosis in each voxel after adjusting for the covariates. RESULTS: The study included 769 participants of 464 men and 305 women (mean age, 63 years ± 12 [standard deviation]). The hazard ratio map indicated that tumors in the medial frontobasal region and around the third and fourth ventricles were associated with poorer prognoses, underscoring the challenges of complete resection and treatment accessibility in these areas regardless of the tumor volume. Conversely, tumors located in the right temporal and occipital lobes had favorable prognoses. CONCLUSION: This study showed an association between tumor location and prognosis. These findings may assist clinicians in developing more precise and effective treatment plans for patients with glioblastoma to improve their management.

DOI： 10.1007/s00234-024-03503-y

PubMed

DOI： 10.18888/rp.0000002803

Publishing type：Research paper (scientific journal)  

Abstract

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

DOI： 10.1007/s11604-024-01668-3

PubMed

Other URL： https://link.springer.com/article/10.1007/s11604-024-01668-3/fulltext.html

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1148/radiol.241162

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development.

DOI： 10.1007/s12149-024-01981-x

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Abstract

Objectives

Large language models like GPT-4 have demonstrated potential for diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals. This study aimed to assess the diagnostic capabilities of GPT-4-based Chat Generative Pre-trained Transformer (ChatGPT) using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists.

Methods

We collected brain MRI reports written in Japanese from preoperative brain tumor patients at two institutions from January 2017 to December 2021. The MRI reports were translated into English by radiologists. GPT-4 and five radiologists were presented with the same textual findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. McNemar’s test and Fisher’s exact test were used for statistical analysis.

Results

In a study analyzing 150 radiological reports, GPT-4 achieved a final diagnostic accuracy of 73%, while radiologists’ accuracy ranged from 65 to 79%. GPT-4’s final diagnostic accuracy using reports from neuroradiologists was higher at 80%, compared to 60% using those from general radiologists. In the realm of differential diagnoses, GPT-4’s accuracy was 94%, while radiologists’ fell between 73 and 89%. Notably, for these differential diagnoses, GPT-4’s accuracy remained consistent whether reports were from neuroradiologists or general radiologists.

Conclusion

GPT-4 exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. GPT-4 can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents.

Clinical relevance statement

This study evaluated GPT-4-based ChatGPT’s diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists.

Key Points

We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors.

GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists.

GPT-4 has the potential to improve the diagnostic process in clinical radiology.



Graphical Abstract

DOI： 10.1007/s00330-024-11032-8

PubMed

Other URL： https://link.springer.com/article/10.1007/s00330-024-11032-8/fulltext.html

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1007/s00330-024-10874-6

PubMed

Other URL： https://link.springer.com/article/10.1007/s00330-024-10874-6/fulltext.html

Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11547-024-01861-4

PubMed

Other URL： https://link.springer.com/article/10.1007/s11547-024-01861-4/fulltext.html

Authorship：Last author   Publishing type：Research paper (scientific journal)  

Abstract

Purpose

The performance of vision-language models (VLMs) with image interpretation capabilities, such as GPT-4 omni (GPT-4o), GPT-4 vision (GPT-4V), and Claude-3, has not been compared and remains unexplored in specialized radiological fields, including nuclear medicine and interventional radiology. This study aimed to evaluate and compare the diagnostic accuracy of various VLMs, including GPT-4 + GPT-4V, GPT-4o, Claude-3 Sonnet, and Claude-3 Opus, using Japanese diagnostic radiology, nuclear medicine, and interventional radiology (JDR, JNM, and JIR, respectively) board certification tests.

Materials and methods

In total, 383 questions from the JDR test (358 images), 300 from the JNM test (92 images), and 322 from the JIR test (96 images) from 2019 to 2023 were consecutively collected. The accuracy rates of the GPT-4 + GPT-4V, GPT-4o, Claude-3 Sonnet, and Claude-3 Opus were calculated for all questions or questions with images. The accuracy rates of the VLMs were compared using McNemar’s test.

Results

GPT-4o demonstrated the highest accuracy rates across all evaluations with the JDR (all questions, 49%; questions with images, 48%), JNM (all questions, 64%; questions with images, 59%), and JIR tests (all questions, 43%; questions with images, 34%), followed by Claude-3 Opus with the JDR (all questions, 40%; questions with images, 38%), JNM (all questions, 42%; questions with images, 43%), and JIR tests (all questions, 40%; questions with images, 30%). For all questions, McNemar’s test showed that GPT-4o significantly outperformed the other VLMs (all P < 0.007), except for Claude-3 Opus in the JIR test. For questions with images, GPT-4o outperformed the other VLMs in the JDR and JNM tests (all P < 0.001), except Claude-3 Opus in the JNM test.

Conclusion

The GPT-4o had the highest success rates for questions with images and all questions from the JDR, JNM, and JIR board certification tests.

DOI： 10.1007/s11604-024-01633-0

PubMed

Other URL： https://link.springer.com/article/10.1007/s11604-024-01633-0/fulltext.html

Authorship：Last author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

OBJECTIVES: To compare the diagnostic accuracy of Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT-4 with vision (GPT-4V) based ChatGPT, and radiologists in musculoskeletal radiology. MATERIALS AND METHODS: We included 106 "Test Yourself" cases from Skeletal Radiology between January 2014 and September 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Two radiologists (a radiology resident and a board-certified radiologist) independently provided diagnoses for all cases. The diagnostic accuracy rates were determined based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. RESULTS: GPT-4-based ChatGPT significantly outperformed GPT-4V-based ChatGPT (p < 0.001) with accuracy rates of 43% (46/106) and 8% (9/106), respectively. The radiology resident and the board-certified radiologist achieved accuracy rates of 41% (43/106) and 53% (56/106). The diagnostic accuracy of GPT-4-based ChatGPT was comparable to that of the radiology resident, but was lower than that of the board-certified radiologist although the differences were not significant (p = 0.78 and 0.22, respectively). The diagnostic accuracy of GPT-4V-based ChatGPT was significantly lower than those of both radiologists (p < 0.001 and < 0.001, respectively). CONCLUSION: GPT-4-based ChatGPT demonstrated significantly higher diagnostic accuracy than GPT-4V-based ChatGPT. While GPT-4-based ChatGPT's diagnostic performance was comparable to radiology residents, it did not reach the performance level of board-certified radiologists in musculoskeletal radiology. CLINICAL RELEVANCE STATEMENT: GPT-4-based ChatGPT outperformed GPT-4V-based ChatGPT and was comparable to radiology residents, but it did not reach the level of board-certified radiologists in musculoskeletal radiology. Radiologists should comprehend ChatGPT's current performance as a diagnostic tool for optimal utilization. KEY POINTS: This study compared the diagnostic performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists in musculoskeletal radiology. GPT-4-based ChatGPT was comparable to radiology residents, but did not reach the level of board-certified radiologists. When utilizing ChatGPT, it is crucial to input appropriate descriptions of imaging findings rather than the images.

DOI： 10.1007/s00330-024-10902-5

PubMed

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.amjcard.2024.05.027

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1016/s2589-7500(24)00113-4

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1001/jamapediatrics.2024.1740

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

DOI： 10.1007/s11604-024-01608-1

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1001/jamaophthalmol.2024.1870

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1016/j.diii.2024.06.002

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1007/s00062-024-01426-y

PubMed

Other URL： https://link.springer.com/article/10.1007/s00062-024-01426-y/fulltext.html

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1148/radiol.233428

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

BACKGROUND: Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another. PURPOSE: This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging. DATA SOURCES: PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images. STUDY SELECTION: This review collected 102 works published between April 2017 and August 2023. DATA ANALYSIS: Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test. DATA SYNTHESIS: Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P < .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions. LIMITATIONS: Our review is limited by the study design and model heterogeneity. CONCLUSIONS: Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.

DOI： 10.3174/ajnr.A8211

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

DOI： 10.1007/s11604-024-01552-0

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

PURPOSE: Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI. METHODS: Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher's exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features. RESULTS: The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002). CONCLUSION: The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

DOI： 10.1007/s00234-024-03322-1

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers were prospectively recruited. DTI and DWI were obtained with six and three directions of the motion probing gradient (MPG), respectively. The identical imaging plane was paired for the image-to-image translation model that synthesized one direction of the MPG from DWI. This process was repeated six times in the respective MPG directions. Regions of interest (ROIs) in the lentiform nucleus, thalamus, posterior limb of the internal capsule, posterior thalamic radiation, and splenium of the corpus callosum were created and applied to maps derived from the original and synthetic DTI. The mean values and signal-to-noise ratio (SNR) of the original and synthetic maps for each ROI were compared. The Bland-Altman plot between the original and synthetic data was evaluated. Although the test dataset showed a larger standard deviation of all values and lower SNR in the synthetic data than in the original data, the Bland-Altman plots showed each plot localizing in a similar distribution. Synthetic DTI could be generated from conventional DWI with an image-to-image translation model.

DOI： 10.1038/s41598-024-53278-8

PubMed

Authorship：Last author,　Corresponding author  

Abstract

Background

The rapid advancement of generative artificial intelligence (AI) has revolutionized understanding and generation of human language. Their integration into healthcare has shown potential for improving medical diagnostics, yet a comprehensive diagnostic performance evaluation of generative AI models and the comparison of their diagnostic performance with that of physicians has not been extensively explored.

Methods

In this systematic review and meta-analysis, a comprehensive search of Medline, Scopus, Web of Science, Cochrane Central, and medRxiv was conducted for studies published from June 2018 through December 2023, focusing on those that validate generative AI models for diagnostic tasks. Meta-analysis was performed to summarize the performance of the models and to compare the accuracy of the models with that of physicians. The quality of studies was assessed using the Prediction Model Study Risk of Bias Assessment Tool.

Results

The search resulted in 54 studies being included in the meta-analysis, with 13 of these also used in the comparative analysis. Eight models were evaluated across 17 medical specialties. The overall accuracy for generative AI models across 54 studies was 57% (95% confidence interval [CI]: 51–63%). The I-squared statistic of 96% signifies a high degree of heterogeneity among the study results. Meta-regression analysis of generative AI models revealed significantly improved accuracy for GPT-4, and reduced accuracy for some specialties such as Neurology, Endocrinology, Rheumatology, and Radiology. The comparison meta-analysis demonstrated that, on average, physicians exceeded the accuracy of the models (difference in accuracy: 14% [95% CI: 8–19%], p-value &lt;0.001). However, in the performance comparison between GPT-4 and physicians, GPT-4 performed slightly higher than non-experts (–4% [95% CI: –10–2%], p-value = 0.173), and slightly underperformed compared to experts (6% [95% CI: –1–13%], p-value = 0.091). The quality assessment indicated a high risk of bias in the majority of studies, primarily due to small sample sizes.

Conclusions

Generative AI exhibits promising diagnostic capabilities, with accuracy varying significantly by model and medical specialty. Although they have not reached the reliability of expert physicians, the findings suggest that generative AI models have the potential to enhance healthcare delivery and medical education, provided they are integrated with caution and their limitations are well-understood. This study also highlights the need for more rigorous research standards and a larger number of cases in the future.

DOI： 10.1101/2024.01.20.24301563

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Abstract

Background

GPT-4-based ChatGPT demonstrates significant potential in various industries; however, its potential clinical applications remain largely unexplored.

Methods

We employed the New England Journal of Medicine (NEJM) quiz “Image Challenge” from October 2021 to March 2023 to assess ChatGPT's clinical capabilities. The quiz, designed for healthcare professionals, tests the ability to analyze clinical scenarios and make appropriate decisions. We evaluated ChatGPT's performance on the NEJM quiz, analyzing its accuracy rate by questioning type and specialty after excluding quizzes which were impossible to answer without images. ChatGPT was first asked to answer without the five multiple-choice options, and then after being given the options.

Results

ChatGPT achieved an 87% (54/62) accuracy without choices and a 97% (60/62) accuracy with choices, after excluding 16 image-based quizzes. Upon analyzing performance by quiz type, ChatGPT excelled in the Diagnosis category, attaining 89% (49/55) accuracy without choices and 98% (54/55) with choices. Although other categories featured fewer cases, ChatGPT's performance remained consistent. It demonstrated strong performance across the majority of medical specialties; however, Genetics had the lowest accuracy at 67% (2/3).

Conclusion

ChatGPT demonstrates potential for diagnostic applications, suggesting its usefulness in supporting healthcare professionals in making differential diagnoses and enhancing AI-driven healthcare.

DOI： 10.1186/s44247-023-00058-5

Other URL： https://link.springer.com/article/10.1186/s44247-023-00058-5/fulltext.html

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/s2589-7500(23)00224-8

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

<p>The integration of deep learning (DL) in breast MRI has revolutionized the field of medical imaging, notably enhancing diagnostic accuracy and efficiency. This review discusses the substantial influence of DL technologies across various facets of breast MRI, including image reconstruction, classification, object detection, segmentation, and prediction of clinical outcomes such as response to neoadjuvant chemotherapy and recurrence of breast cancer. Utilizing sophisticated models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, DL has improved image quality and precision, enabling more accurate differentiation between benign and malignant lesions and providing deeper insights into disease behavior and treatment responses. DL’s predictive capabilities for patient-specific outcomes also suggest potential for more personalized treatment strategies. The advancements in DL are pioneering a new era in breast cancer diagnostics, promising more personalized and effective healthcare solutions. Nonetheless, the integration of this technology into clinical practice faces challenges, necessitating further research, validation, and development of legal and ethical frameworks to fully leverage its potential.</p>

DOI： 10.2463/mrms.rev.2024-0056

PubMed

Authorship：Last author,　Corresponding author  

Abstract

Objective

To compare the diagnostic accuracy of Generative Pre-trained Transformer (GPT)-4 based ChatGPT, GPT-4 with vision (GPT-4V) based ChatGPT, and radiologists in musculoskeletal radiology.

Materials and Methods

We included 106 “Test Yourself” cases fromSkeletal Radiologybetween January 2014 and September 2023. We input the medical history and imaging findings into GPT-4 based ChatGPT and the medical history and images into GPT-4V based ChatGPT, then both generated a diagnosis for each case. Two radiologists (a radiology resident and a board-certified radiologist) independently provided diagnoses for all cases. The diagnostic accuracy rates were determined based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4 based ChatGPT, GPT-4V based ChatGPT, and radiologists.

Results

GPT-4 based ChatGPT significantly outperformed GPT-4V based ChatGPT (p&lt; 0.001) with accuracy rates of 43% (46/106) and 8% (9/106), respectively. The radiology resident and the board-certified radiologist achieved accuracy rates of 41% (43/106) and 53% (56/106). The diagnostic accuracy of GPT-4 based ChatGPT was comparable to that of the radiology resident but was lower than that of the board-certified radiologist, although the differences were not significant (p= 0.78 and 0.22, respectively). The diagnostic accuracy of GPT-4V based ChatGPT was significantly lower than those of both radiologists (p&lt; 0.001 and &lt; 0.001, respectively).

Conclusion

GPT-4 based ChatGPT demonstrated significantly higher diagnostic accuracy than GPT-4V based ChatGPT. While GPT-4 based ChatGPT’s diagnostic performance was comparable to radiology residents, it did not reach the performance level of board-certified radiologists in musculoskeletal radiology.

DOI： 10.1101/2023.12.07.23299707

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications in neuroradiology remain unexplored. This study aimed to evaluate the diagnostic performance of GPT-4 based ChatGPT in neuroradiology. METHODS: We collected 100 consecutive "Case of the Week" cases from the American Journal of Neuroradiology between October 2021 and September 2023. ChatGPT generated a diagnosis from patient's medical history and imaging findings for each case. Then the diagnostic accuracy rate was determined using the published ground truth. Each case was categorized by anatomical location (brain, spine, and head & neck), and brain cases were further divided into central nervous system (CNS) tumor and non-CNS tumor groups. Fisher's exact test was conducted to compare the accuracy rates among the three anatomical locations, as well as between the CNS tumor and non-CNS tumor groups. RESULTS: ChatGPT achieved a diagnostic accuracy rate of 50% (50/100 cases). There were no significant differences between the accuracy rates of the three anatomical locations (p = 0.89). The accuracy rate was significantly lower for the CNS tumor group compared to the non-CNS tumor group in the brain cases (16% [3/19] vs. 62% [36/58], p < 0.001). CONCLUSION: This study demonstrated the diagnostic performance of ChatGPT in neuroradiology. ChatGPT's diagnostic accuracy varied depending on disease etiologies, and its diagnostic accuracy was significantly lower in CNS tumors compared to non-CNS tumors.

DOI： 10.1007/s00234-023-03252-4

PubMed

Publishing type：Research paper (scientific journal)  

Abstract

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist’s perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

DOI： 10.1093/jrr/rrad090

PubMed

Abstract

Background

Although rotation atherectomy (RA) is a useful technique for severely calcified lesions, patients undergoing RA show a greater incidence of catastrophic complications, such as coronary perforation. Therefore, prior to the RA procedure, it is important to predict which regions of the coronary plaque will be debulked by RA.

Objectives

We develop and evaluate an artificial intelligence–based algorithm that uses pre-RA intravascular ultrasound (IVUS) images to automatically predict regions debulked by RA

Methods

A total of 2106 IVUS cross-sections from 60 patients with de novo severely calcified coronary lesions who underwent IVUS-guided RA were consecutively collected. The two identical IVUS images of pre-and post-RA were merged, and the orientations of the debulked segments identified in the merged images are marked on the outer circle of each IVUS image. The artificial intelligence model was developed based on ResNet (deep residual learning for image recognition). The architecture connected 36 fully connected layers, each corresponding to one of the 36 orientations segmented every 10°, to a single feature extractor.

Results

In each cross-sectional analysis, our artificial intelligence model achieved an average sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 72%, 46%, 90%, and 75%, respectively.

Conclusions

The artificial intelligence–based algorithm can use information from pre-RA IVUS images to accurately predict regions debulked by RA. The proposed method will assist interventional cardiologists in determining the treatment strategies for severely calcified coronary lesions.

DOI： 10.1101/2023.11.07.23298239

Authorship：Last author   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jvir.2023.11.014

PubMed

Authorship：Last author,　Corresponding author  

Abstract

Background

Large Language Models like Chat Generative Pre-trained Transformer (ChatGPT) have demonstrated potential for differential diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals, which may not accurately represent real-world clinical scenarios.

Purpose

This study aimed to assess the diagnostic capabilities of ChatGPT using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists.

Methods

We consecutively collected brain MRI reports from preoperative brain tumor patients at Osaka Metropolitan University Hospital, taken from January to December 2021. ChatGPT and five radiologists were presented with the same findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. Chi-square tests and Fisher’s exact test were used for statistical analysis.

Results

In a study analyzing 99 radiological reports, ChatGPT achieved a final diagnostic accuracy of 75% (95% CI: 66, 83%), while radiologists’ accuracy ranged from 64% to 82%. ChatGPT’s final diagnostic accuracy using reports from neuroradiologists was higher at 82% (95% CI: 71, 89%), compared to 52% (95% CI: 33, 71%) using those from general radiologists with a p-value of 0.012. In the realm of differential diagnoses, ChatGPT’s accuracy was 95% (95% CI: 91, 99%), while radiologists’ fell between 74% and 88%. Notably, for these differential diagnoses, ChatGPT’s accuracy remained consistent whether reports were from neuroradiologists (96%, 95% CI: 89, 99%) or general radiologists (91%, 95% CI: 73, 98%) with a p-value of 0.33.

Conclusion

ChatGPT exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. ChatGPT can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents, especially for understanding diagnostic cues and handling challenging cases.

Summary

This study evaluated ChatGPT’s diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists.

Key results

ChatGPT demonstrated a diagnostic accuracy rate of 75% for final diagnoses based on preoperative MRI findings from 99 brain tumor cases, competing favorably with five radiologists whose accuracies ranged between 64% and 82%. For differential diagnoses, ChatGPT achieved a remarkable 95% accuracy, outperforming several of the radiologists.

Radiology reports from neuroradiologists and general radiologists showed varying accuracy when input into ChatGPT. Reports from neuroradiologists resulted in higher diagnostic accuracy for final diagnoses, while there was no difference in accuracy for differential diagnoses between neuroradiologists and general radiologists.

DOI： 10.1101/2023.10.27.23297585

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

DOI： 10.1007/s11547-023-01691-w

PubMed

Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

DOI： 10.1007/s12149-023-01865-6

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1016/s2666-7568(23)00133-2

PubMed

Authorship：Last author,　Corresponding author  

Abstract

Purpose

To compare the diagnostic performance between Chat Generative Pre-trained Transformer (ChatGPT), based on the GPT-4 architecture, and radiologists from patient’s medical history and imaging findings in challenging neuroradiology cases.

Methods

We collected 30 consecutive “Freiburg Neuropathology Case Conference” cases from the journal Clinical Neuroradiology between March 2016 and June 2023. GPT-4 based ChatGPT generated diagnoses from the patient’s provided medical history and imaging findings for each case, and the diagnostic accuracy rate was determined based on the published ground truth. Three radiologists with different levels of experience (2, 4, and 7 years of experience, respectively) independently reviewed all the cases based on the patient’s provided medical history and imaging findings, and the diagnostic accuracy rates were evaluated. The Chi-square tests were performed to compare the diagnostic accuracy rates between ChatGPT and each radiologist.

Results

ChatGPT achieved an accuracy rate of 23% (7/30 cases). Radiologists achieved the following accuracy rates: a junior radiology resident had 27% (8/30) accuracy, a senior radiology resident had 30% (9/30) accuracy, and a board-certified radiologist had 47% (14/30) accuracy. ChatGPT’s diagnostic accuracy rate was lower than that of each radiologist, although the difference was not significant (p= 0.99, 0.77, and 0.10, respectively).

Conclusion

The diagnostic performance of GPT-4 based ChatGPT did not reach the performance level of either junior/senior radiology residents or board-certified radiologists in challenging neuroradiology cases. While ChatGPT holds great promise in the field of neuroradiology, radiologists should be aware of its current performance and limitations for optimal utilization.

DOI： 10.1101/2023.08.28.23294607

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

DOI： 10.1007/s11604-023-01474-3

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Background Carbon 11 (11C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set). Pearson correlation coefficients for the maximum and mean tumor to background ratio (TBRmax and TBRmean, respectively) of methionine uptake and the lesion volume between synthetic and real PET were calculated. Two additional open-source glioma databases of preoperative CE MRI without methionine PET were used as the external test set. Using the TBRs, the area under the receiver operating characteristic curve (AUC) for classifying high-grade and low-grade gliomas and overall survival were evaluated. Results The institutional data set included 362 patients (mean age, 49 years ± 19 [SD]; 195 female, 167 male; training, n = 294; validation, n = 34; test, n = 34). In the internal test set, Pearson correlation coefficients were 0.68 (95% CI: 0.47, 0.81), 0.76 (95% CI: 0.59, 0.86), and 0.92 (95% CI: 0.85, 0.95) for TBRmax, TBRmean, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, n = 269). The AUC for TBRmax was 0.81 (95% CI: 0.75, 0.86) and the overall survival analysis showed a significant difference between the high (2-year survival rate, 27%) and low (2-year survival rate, 71%; P < .001) TBRmax groups. Conclusion The AI-based model-generated synthetic methionine PET images strongly correlated with real PET images and showed good performance for glioma grading and prognostication. Published under a CC BY 4.0 license. Supplemental material is available for this article.

DOI： 10.1148/radiol.223016

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1016/s2589-7500(23)00107-3

PubMed

Publishing type：Research paper (scientific journal)  

DOI： 10.1002/jmri.28892

PubMed

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.diii.2023.06.011

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

DOI： 10.1148/radiol.231040

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Background

Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.

Methods

A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysisviaa hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool.

Results

In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96–0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79–89%) for DL and 85% (95% CI 73–92%) for physicians and the pooled specificity was 96% (95% CI 94–98%) for DL and 98% (95% CI 95–99%) for physicians. More than half of the original studies (57%) had a high risk of bias.

Conclusions

Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.

DOI： 10.1183/16000617.0259-2022

PubMed

Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11547-023-01638-1

PubMed

Other URL： https://link.springer.com/article/10.1007/s11547-023-01638-1/fulltext.html

Authorship：Lead author,　Corresponding author  

Background GPT-4-based ChatGPT demonstrates significant potential in various industries; however, its potential clinical applications remain largely unexplored. Methods We employed the New England Journal of Medicine (NEJM) quiz "Image Challenge" from October 2021 to March 2023 to assess ChatGPT's clinical capabilities. The quiz, designed for healthcare professionals, tests the ability to analyze clinical scenarios and make appropriate decisions. We evaluated ChatGPT's performance on the NEJM quiz, analyzing its accuracy rate by questioning type and specialty after excluding quizzes which were impossible to answer without images. The NEJM quiz has five multiple-choice options, but ChatGPT was first asked to answer without choices, and then given the choices to answer afterwards, in order to evaluate the accuracy in both scenarios. Results ChatGPT achieved an 87% accuracy without choices and a 97% accuracy with choices, after excluding 16 image-based quizzes. Upon analyzing performance by quiz type, ChatGPT excelled in the Diagnosis category, attaining 89% accuracy without choices and 98% with choices. Although other categories featured fewer cases, ChatGPT's performance remained consistent. It demonstrated strong performance across the majority of medical specialties; however, Genetics had the lowest accuracy at 67%. Conclusion ChatGPT demonstrates potential for clinical application, suggesting its usefulness in supporting healthcare professionals and enhancing AI-driven healthcare.

DOI： 10.1101/2023.05.04.23289493

Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.jos.2023.05.004

PubMed

Publishing type：Research paper (scientific journal)  

We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence. Artificial intelligence models were implemented using convolutional neural networks and multilayer perceptron as a classifier. Furthermore, the Youden index was used to discriminate between high- and low-risk groups. The importance values of each explanatory variable for early recurrence were calculated using permutation importance. The DL predictive model for postoperative early recurrence was developed with the area under the curve values of 0.71 (test datasets) and 0.73 (validation datasets). Postoperative early recurrence incidences in the high- and low-risk groups were 73% and 30%, respectively (p = 0.0057). Permutation importance demonstrated that among the explanatory variables, the variable with the highest importance value was CECT imaging analysis. We developed a DL model to predict postoperative early HCC recurrence. DL-based analysis is effective for determining the treatment strategies in patients with HCC.

DOI： 10.3390/cancers15072140

PubMed

拡散テンソルイメージング(DTI)データの画像再構成により、analysis along the perivascular space(ALPS) indexにおいて再現性の改善を試みた。なお、本報では脳MRI画像データが登録されたオープンデータベースOASIS-3データセットから、認知機能が正常な234名(男性123名、女性111名、年齢42～92歳)の画像データを用いて実施した。また、DTIのベクトル情報を他の血管周囲腔に位置合わせする画像再構成技術を適用し、元データのALPS indexと再構成したALPS indexを新たに求め、再構成処理したdiffusivity mapを作成した。これらデータ間を比較した結果、再構成処理したALPS indexの変動は元データのALPS indexに比べ有意に小さく、再構成技術により、頭部回転を有する被験者でも良好ないし優れたALPS index数値の再現性が得られた。特にx軸で頭部回転が観察された被験者では、元データALPS indexと再構成したALPS indexを比較したBland-Altmanプロットにより、広範囲に及ぶ95%一致限界が認められたことから、x軸の頭部回転はALPS indexの算出に著しく影響を及ぼすことが示唆された。以上より、本法による再構成技術を適用したDTI-ALPS index算出によりALPSの再現性が改善され、再構成処理したdiffusivity mapも作成可能となった。

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

DOI： 10.1007/s10278-022-00691-y

PubMed

Other URL： https://link.springer.com/article/10.1007/s10278-022-00691-y/fulltext.html

DOI： 10.1148/radiol.232082

<p>In recent years, the rapid evolution of artificial intelligence (AI) has brought about a revolution in medical research. In particular, the application of AI technology to medical imaging is expanding rapidly, with image-to-image translation technique gaining significant attention. Image-to-image translation technique allows for a wide range of applications, such as converting between different imaging modalities and removing artifacts. It is expected to open up new perspectives that go beyond the traditional framework of medical imaging. Using image-to-image translation models, it’s possible to generate synthetic PET from MRI images, or convert images with artifacts to those without, potentially contributing to improved diagnostic accuracy and optimization of treatment plans. In this article, we introduce two papers we published applying image-to-image translation technique in the field of neuroradiology: a study on generating synthetic methionine PET using MRI, and a study on producing Digital Subtraction Angiography (DSA) without misregistration artifacts.</p>

DOI： 10.11318/mii.40.66

Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

DOI： 10.2463/mrms.rev.2023-0047

Authorship：Last author,　Corresponding author  

DOI： 10.48550/arXiv.2212.11113

Publishing type：Research paper (scientific journal)  

Abstract

Purpose

Diffusion tensor image analysis along the perivascular space (DTI-ALPS) index is intended to reflect the glymphatic function of the brain; however, head rotation may reduce reproducibility and reliability. This study aimed to evaluate whether reorientation of DTI data improves the reproducibility of the ALPS index using the OASIS-3 dataset.

Materials and methods

234 cognitively normal subjects from the OASIS-3 dataset were included. Original and reoriented ALPS indices were calculated using a technique that registered vector information of DTI to another space and created reoriented diffusivity maps. The F test was used to compare variances of the original and reoriented ALPS indices. Subsequently, subjects with head rotation around the z- (inferior-superior; n = 43) or x axis (right-left; n = 25) and matched subjects with neutral head position were selected for evaluation of intra- and inter-rater reliability. Intraclass correlation coefficients (ICCs) of the original and reoriented ALPS indices for participants with head rotation and neutral head position were calculated separately. The Bland–Altman plot comparing the original and reoriented ALPS indices was also evaluated.

Results

The reoriented ALPS index exhibited a significantly smaller variance than the original ALPS index (p &lt; 0.001). For intra- and inter-reliability, the reorientation technique showed good-to-excellent reproducibility in calculating the ALPS index even in subjects with head rotation (ICCs of original ALPS index: 0.52–0.81; ICCs of reoriented ALPS index: &gt; 0.85). A wider range of the 95% limit of agreement of the Bland–Altman plot for subjects with x axis rotation was identified, indicating that x axis rotation may remarkably affect calculation of the ALPS index.

Conclusion

The technique used in this study enabled the creation of reoriented diffusivity maps and improved reproducibility in calculating the ALPS index.

DOI： 10.1007/s11604-022-01370-2

PubMed

Other URL： https://link.springer.com/article/10.1007/s11604-022-01370-2/fulltext.html

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Objectives:

The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs.

Methods:

This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHAP values.

Results:

The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72–0.86), 0.74 (0.68–0.79), 0.77 (0.61–0.88), and 0.74 (0.69–0.79) for the clinical data-based model; 0.77 (0.69–0.85), 0.67 (0.61–0.73), 0.81 (0.67–0.92), 0.70 (0.64–0.75) for the image-based model, and 0.86 (0.81–0.91), 0.76 (0.70–0.81), 0.77 (0.61–0.88), 0.76 (0.70–0.81) for the mixed model. The mixed model had the best performance (p value &lt; 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed.

Conclusions:

These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together.

Advances in knowledge:

This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.

DOI： 10.1259/bjr.20220058

PubMed

症例は79歳女性。50歳代時に虫垂炎のため虫垂切除を行った。75歳時に子宮頸部扁平上皮細胞癌と診断され、放射線療法と化学療法を行い、治療の42ヵ月後の経過観察では再発は認められなかった。診察により左下部に圧痛が認められた。上腹部レントゲン検査でニボーを伴う弛緩性ループが観察され、当初は癒着性の小腸閉塞と診断された。保存療法では症状が変化しなかったため、腹部と骨盤の造影多列検出器コンピューター断層撮影を行った。その結果、S状結腸間膜の欠如による腸間膜脂肪組織と腸間膜血管による小腸への陥入が観察され、絞扼性小腸閉塞(TMSH)と診断した。緊急開腹手術を行い、TMSHが確認された。小腸の壊死部分を切除し、術後9日目に退院した。

Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

<title>Abstract</title>We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50–0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI.

DOI： 10.1038/s41598-021-04667-w

Other URL： https://www.nature.com/articles/s41598-021-04667-w

Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

PURPOSE: To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography. METHOD: Mammograms were consecutively collected from 5080 patients who underwent breast cancer screening from January 2015 to March 2017. Of these, 93 patients (136 breasts, mean age, 50 ± 7 years) had microcalcifications in their breasts on mammograms. We applied an artificial intelligence model known as a fast SR convolutional neural network to the mammograms. SR and original mammograms were visually evaluated by four breast radiologists using a 5-point scale (1: original mammograms are strongly preferred, 5: SR mammograms are strongly preferred) for the detection, diagnostic quality, contrast, sharpness, and noise of microcalcifications. Mammograms were quantitatively evaluated using a perception-based image-quality evaluator (PIQE). RESULTS: All radiologists rated the SR mammograms better than the original ones in terms of detection, diagnostic quality, contrast, and sharpness of microcalcifications. These ratings were significantly different according to the Wilcoxon signed-rank test (p <.001), while the noise score of the three radiologists was significantly lower (p <.001). According to PIQE, SR mammograms were rated better than the original mammograms, showing a significant difference by paired t-test (p <.001). CONCLUSION: An SR model based on deep learning can improve the visibility of microcalcifications in mammography and help detect and diagnose them in mammograms.

DOI： 10.1016/j.ejrad.2022.110433

PubMed

Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

OBJECTIVE: The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs. METHODS: This retrospective study included consecutively collected chest radiographs of patients who had echocardiography at our institution from July 2016 to May 2019. Eligible radiographs had been acquired within 30 days of the echocardiography. These radiographs were labeled as AF-positive or AF-negative based on the associated electronic medical records; then, each patient was randomly divided into training, validation, and test datasets in an 8:1:1 ratio. A deep learning-based model to classify radiographs as with or without AF was trained on the training dataset, tuned with the validation dataset, and evaluated with the test dataset. RESULTS: The training dataset included 11,105 images (5637 patients; 3145 male, mean age ± standard deviation, 68 ± 14 years), the validation dataset included 1388 images (704 patients, 397 male, 67 ± 14 years), and the test dataset included 1375 images (706 patients, 395 male, 68 ± 15 years). Applying the model to the validation and test datasets gave a respective area under the curve of 0.81 (95% confidence interval, 0.78-0.85) and 0.80 (0.76-0.84), sensitivity of 0.76 (0.70-0.81) and 0.70 (0.64-0.76), specificity of 0.75 (0.72-0.77) and 0.74 (0.72-0.77), and accuracy of 0.75 (0.72-0.77) and 0.74 (0.71-0.76). CONCLUSION: Our AI can identify AF on chest radiographs, which provides a new way for radiologists to infer AF. KEY POINTS: • A deep learning-based model was trained to detect atrial fibrillation in chest radiographs, showing that there are indicators of atrial fibrillation visible even on static images. • The validation and test datasets each gave a solid performance with area under the curve, sensitivity, and specificity of 0.81, 0.76, and 0.75, respectively, for the validation dataset, and 0.80, 0.70, and 0.74, respectively, for the test dataset. • The saliency maps highlighted anatomical areas consistent with those reported for atrial fibrillation on chest radiographs, such as the atria.

DOI： 10.1007/s00330-022-08752-0

PubMed

Other URL： https://link.springer.com/article/10.1007/s00330-022-08752-0/fulltext.html

Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

PURPOSE: To develop a deep learning (DL) model to generate synthetic, 2-dimensional subtraction angiograms free of artifacts from native abdominal angiograms. MATERIALS AND METHODS: In this retrospective study, 2-dimensional digital subtraction angiography (2D-DSA) images and native angiograms were consecutively collected from July 2019 to March 2020. Images were divided into motion-free (training, validation, and motion-free test datasets) and motion-artifact (motion-artifact test dataset) sets. A total of 3,185, 393, 383, and 345 images from 87 patients (mean age, 71 years ± 10; 64 men and 23 women) were included in the training, validation, motion-free, and motion-artifact test datasets, respectively. Native angiograms and 2D-DSA image pairs were used to train and validate an image-to-image translation model to generate synthetic DL-based subtraction angiography (DLSA) images. DLSA images were quantitatively evaluated by the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) using the motion-free dataset and were qualitatively evaluated via visual assessments by radiologists with a numerical rating scale using the motion-artifact dataset. RESULTS: The DLSA images showed a mean PSNR (± standard deviation) of 43.05 dB ± 3.65 and mean SSIM of 0.98 ± 0.01, indicating high agreement with the original 2D-DSA images in the motion-free dataset. Qualitative visual evaluation by radiologists of the motion-artifact dataset showed that DLSA images contained fewer motion artifacts than 2D-DSA images. Additionally, DLSA images scored similar to or higher than 2D-DSA images for vascular visualization and clinical usefulness. CONCLUSIONS: The developed DL model generated synthetic, motion-free subtraction images from abdominal angiograms with similar imaging characteristics to 2D-DSA images.

DOI： 10.1016/j.jvir.2022.03.010

PubMed

副甲状腺機能亢進症患者の早期相ならびに後期相の99mTc-MIBIシンチグラフィー画像から副甲状腺腺腫を検出するため、深層学習(DL)をベースとしたモデルをそれぞれ開発し、検証した。DLモデルの学習データセットには患者192例(男性47例、女性145例、平均年齢64±13歳)から検出された165腺腫を選択し、検証データセットには、患者45例(男性8例、女性37例、平均年齢67±12歳)から検出された30腺腫とした。画像は2010年6月～2019年3月までの期間内に著者等の医療施設で、シンチグラフィーと超音波検査を受けた患者から収集し、試験データセットには、患者44例(男性6例、女性38例、平均年齢67±12歳)から検出された30腺腫を用いて解析した。その結果、初期相モデルによるシンチグラムでは病変をベースとした感度は82%[95%CI 72-92%]、1画像当りの平均偽陽性数(mFPI)は0.44で、後期相モデルではそれぞれ83%[95%CI 73-92%]、0.31であった。これらの所見から、99mTc-MIBIシンチグラフィー画像を用いて、本DLモデルを適用することにより、高感度に副甲状腺腺腫が検出されることが確認された。

Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

OBJECTIVE: It is important to detect parathyroid adenomas by parathyroid scintigraphy with 99m-technetium sestamibi (99mTc-MIBI) before surgery. This study aimed to develop and validate deep learning (DL)-based models to detect parathyroid adenoma in patients with primary hyperparathyroidism, from parathyroid scintigrams with 99mTc-MIBI. METHODS: DL-based models for detecting parathyroid adenoma in early- and late-phase parathyroid scintigrams were, respectively, developed and evaluated. The training dataset used to train the models was collected from 192 patients (165 adenoma cases, mean age: 64 years ± 13, 145 women) and the validation dataset used to tune the models was collected from 45 patients (30 adenoma cases, mean age: 67 years ± 12, 37 women). The images were collected from patients who were pathologically diagnosed with parathyroid adenomas or in whom no lesions could be detected by either parathyroid scintigraphy or ultrasonography at our institution from June 2010 to March 2019. The models were tested on a dataset collected from 44 patients (30 adenoma cases, mean age: 67 years ± 12, 38 women) who took scintigraphy from April 2019 to March 2020. The models' lesion-based sensitivity and mean false positive indications per image (mFPI) were assessed with the test dataset. RESULTS: The sensitivity was 82% [95% confidence interval 72-92%] with mFPI of 0.44 for the scintigrams of the early-phase model and 83% [73-92%] with mFPI of 0.31 for the scintigrams of the delayed-phase model in the test dataset, respectively. CONCLUSIONS: The DL-based models were able to detect parathyroid adenomas with a high sensitivity using parathyroid scintigraphy with 99m-technetium sestamibi.

DOI： 10.1007/s12149-022-01726-8

PubMed

Other URL： https://link.springer.com/article/10.1007/s12149-022-01726-8/fulltext.html

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Objectives

The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography.

Methods

Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model’s sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets.

Results

The hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45–0.47 mFPI and had partial AUCs of 0.93 in both test datasets.

Conclusions

The DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.

DOI： 10.1371/journal.pone.0265751

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

DOI： 10.1148/ryai.210221

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)  

Abstract

Aims

We aimed to develop models to detect aortic stenosis (AS) from chest radiographs—one of the most basic imaging tests—with artificial intelligence.

Methods and results

We used 10 433 retrospectively collected digital chest radiographs from 5638 patients to train, validate, and test three deep learning models. Chest radiographs were collected from patients who had also undergone echocardiography at a single institution between July 2016 and May 2019. These were labelled from the corresponding echocardiography assessments as AS-positive or AS-negative. The radiographs were separated on a patient basis into training [8327 images from 4512 patients, mean age 65 ±  (standard deviation) 15 years], validation (1041 images from 563 patients, mean age 65 ± 14 years), and test (1065 images from 563 patients, mean age 65 ± 14 years) datasets. The soft voting-based ensemble of the three developed models had the best overall performance for predicting AS with an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 0.83 (95% confidence interval 0.77–0.88), 0.78 (0.67–0.86), 0.71 (0.68–0.73), 0.71 (0.68–0.74), 0.18 (0.14–0.23), and 0.97 (0.96–0.98), respectively, in the validation dataset and 0.83 (0.78–0.88), 0.83 (0.74–0.90), 0.69 (0.66–0.72), 0.71 (0.68–0.73), 0.23 (0.19–0.28), and 0.97 (0.96–0.98), respectively, in the test dataset.

Conclusion

Deep learning models using chest radiographs have the potential to differentiate between radiographs of patients with and without AS.

Lay Summary

We created artificial intelligence (AI) models using deep learning to identify aortic stenosis (AS) from chest radiographs. Three AI models were developed and evaluated with 10 433 retrospectively collected radiographs and labelled from echocardiography reports. The ensemble AI model could detect AS in a test dataset with an area under the receiver operating characteristic curve of 0.83 (95% confidence interval 0.78–0.88). Since chest radiography is a cost-effective and widely available imaging test, our model can provide an additive resource for the detection of AS.

DOI： 10.1093/ehjdh/ztab102

Other URL： https://academic.oup.com/ehjdh/article-pdf/3/1/20/47116703/ztab102.pdf

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

<sec><title>PURPOSE</title> The molecular subtype of breast cancer is an important component of establishing the appropriate treatment strategy. In clinical practice, molecular subtypes are determined by receptor expressions. In this study, we developed a model using deep learning to determine receptor expressions from mammograms.

</sec><sec><title>METHODS</title> A developing data set and a test data set were generated from mammograms from the affected side of patients who were pathologically diagnosed with breast cancer from January 2006 through December 2016 and from January 2017 through December 2017, respectively. The developing data sets were used to train and validate the DL-based model with five-fold cross-validation for classifying expression of estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2-neu (HER2). The area under the curves (AUCs) for each receptor were evaluated with the independent test data set.

</sec><sec><title>RESULTS</title> The developing data set and the test data set included 1,448 images (997 ER-positive and 386 ER-negative, 641 PgR-positive and 695 PgR-negative, and 220 HER2-enriched and 1,109 non–HER2-enriched) and 225 images (176 ER-positive and 40 ER-negative, 101 PgR-positive and 117 PgR-negative, and 53 HER2-enriched and 165 non–HER2-enriched), respectively. The AUC of ER-positive or -negative in the test data set was 0.67 (0.58-0.76), the AUC of PgR-positive or -negative was 0.61 (0.53-0.68), and the AUC of HER2-enriched or non–HER2-enriched was 0.75 (0.68-0.82).

</sec><sec><title>CONCLUSION</title> The DL-based model effectively classified the receptor expressions from the mammograms. Applying the DL-based model to predict breast cancer classification with a noninvasive approach would have additive value to patients.

</sec>

DOI： 10.1200/PO.20.00176

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

<title>Abstract</title><sec>
<title>Background</title>
We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors.


</sec><sec>
<title>Methods</title>
Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers’ assessments were calculated.


</sec><sec>
<title>Results</title>
In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader’s sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14–1.30), 1.00 (1.00–1.01), 1.03 (1.02–1.04), 1.07 (1.03–1.11), and 1.02 (1.01–1.03) by using the CAD, respectively.


</sec><sec>
<title>Conclusion</title>
The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.


</sec>

DOI： 10.1186/s12885-021-08847-9

PubMed

Other URL： https://link.springer.com/article/10.1186/s12885-021-08847-9/fulltext.html

Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

BACKGROUND AND AIMS: We developed a deep learning (DL) model for automated atherosclerotic plaque categorization using optical frequency domain imaging (OFDI) and performed quantitative and visual evaluations. METHODS: A total of 1103 histological cross-sections from 45 autopsy hearts were examined to compare the ex vivo OFDI scans. The images were segmented and annotated considering four histological categories: pathological intimal thickening (PIT), fibrous cap atheroma (FA), fibrocalcific plaque (FC), and healed erosion/rupture (HER). The DL model was developed based on pyramid scene parsing network (PSPNet). Given an input image, a convolutional neural network (ResNet50) was used as an encoder to generate feature maps of the last convolutional layer. RESULTS: For the quantitative evaluation, the mean F-score and IoU values, which are used to evaluate how close the predicted results are to the ground truth, were used. The validation and test dataset had F-score and IoU values of 0.63, 0.49, and 0.66, 0.52, respectively. For the section-level diagnostic accuracy, the areas under the receiver-operating characteristic curve produced by the DL model for FC, PIT, FA, and HER were 0.91, 0.85, 0.86, and 0.86, respectively, and were comparable to those of an expert observer. CONCLUSIONS: DL semantic segmentation of coronary plaques in OFDI images was used as a tool to automatically categorize atherosclerotic plaques using histological findings as the gold standard. The proposed method can support interventional cardiologists in understanding histological properties of plaques.

DOI： 10.1016/j.atherosclerosis.2021.06.003

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose To train and to validate a deep learning (DL)-based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness. Materials and Methods A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 per patient. The development data set was divided into training and validation data sets at ratio of 3:1 per patient. The DL model was created by using the training data set, tuned with the validation data set, and then evaluated quantitatively with the external test data set and visually with the misregistration test data set. Quantitative evaluations used the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) with mixed liner models. Visual evaluation was conducted by using a numerical rating scale. Results The training, validation, nonmisregistration test, and misregistration test data sets included 10 751, 2784, 1346, and 711 paired images collected from 40 patients (mean age, 62 years ± 11 [standard deviation]; 33 women). In the quantitative evaluation, DL-generated angiograms showed a mean PSNR value of 40.2 dB ± 4.05 and a mean SSIM value of 0.97 ± 0.02, indicating high coincidence with the paired DSA images. In the visual evaluation, the median ratings of the DL-generated angiograms were similar to or better than those of the original DSA images for all 24 sequences. Conclusion The deep learning-based model provided clinically useful cerebral angiograms free from clinically significant artifacts directly from dynamic angiograms. Published under a CC BY 4.0 license. Supplemental material is available for this article.

DOI： 10.1148/radiol.2021203692

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

PURPOSE: To demonstrate how artificial intelligence (AI) can expand radiologists' capacity, we visualized the features of invasive ductal carcinomas (IDCs) that our algorithm, developed and validated for basic pathological classification on mammograms, had focused on. MATERIALS AND METHODS: IDC datasets were built using mammograms from patients diagnosed with IDCs from January 2006 to December 2017. The developing dataset was used to train and validate a VGG-16 deep learning (DL) network. The true positives (TPs) and accuracy of the algorithm were externally evaluated using the test dataset. A visualization technique was applied to the algorithm to determine which malignant findings on mammograms were revealed. RESULTS: The datasets were split into a developing dataset (988 images) and a test dataset (131 images). The proposed algorithm diagnosed 62 TPs with an accuracy of 0.61-0.70. The visualization of features on the mammograms revealed that the tubule forming, solid, and scirrhous types of IDCs exhibited visible features on the surroundings, corners of the masses, and architectural distortions, respectively. CONCLUSION: We successfully showed that features isolated by a DL-based algorithm trained to classify IDCs were indeed those known to be associated with each pathology. Thus, using AI can expand the capacity of radiologists through the discovery of previously unknown findings.

DOI： 10.1007/s11604-020-01070-9

PubMed

Other URL： http://link.springer.com/article/10.1007/s11604-020-01070-9/fulltext.html

マンモグラム上での浸潤性乳管癌(IDC)の病理分類のため、深層学習(DL)をベースとしたアルゴリズムの開発と検証に人工知能(AI)を適用し、その有用性について検討した。なお、学習データセットは2006年1月～2016年12月までの期間内に、当院で乳癌と診断された患者529例(年齢25～97歳)から得たマンモグラフィ画像988画像を用いて作成した。VGG-16DLネットワークにより、学習データセットと検証データセットを作成し、テストデータセットには2017年1月～2017年12月までの期間内に、乳癌と診断された当院患者67例(年齢40～92歳)から得た131画像を用いて作成し、アルゴリズムの真陽性と正確さを判定した。可視化技術をアルゴリズムに適用し、マンモグラム上で判明した悪性所見を調査した結果、DLベースのアルゴリズムにより、62画像の真陽性が0.61～0.70の正確さで診断され、マンモグラム上で認められた特徴の可視化により、腺管形成型、充実型および硬性型のIDCが、それぞれ腫瘤の周囲組織や乳腺の歪み、構築の乱れ等、可視化される特徴で描出されることが確認された。以上の所見から、DLベースのアルゴリズムにより、各分類でIDC病理との関連性が指摘されている画像特徴が抽出され、AIの適用により、これまで不明であった所見が明らかにされた。

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：International journal  

Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. Results The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm ± 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years ± 13) and 365 were on female patients (mean age, 64 years ± 13). Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm ± 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years ± 12 and 67 years ± 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm ± 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years ± 12 and 68 years ± 12, respectively). The sensitivity was 91% (592 of 649) and 93% (74 of 80) for the internal and external test data sets, respectively. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports. Conclusion A deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports. © RSNA, 2018 See also the editorial by Flanders in this issue.

DOI： 10.1148/radiol.2018180901

PubMed

Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   International / domestic magazine：Domestic journal  

Deep learning has been applied to clinical applications in not only radiology, but also all other areas of medicine. This review provides a technical and clinical overview of deep learning in radiology. To gain a more practical understanding of deep learning, deep learning techniques are divided into five categories: classification, object detection, semantic segmentation, image processing, and natural language processing. After a brief overview of technical network evolutions, clinical applications based on deep learning are introduced. The clinical applications are then summarized to reveal the features of deep learning, which are highly dependent on training and test datasets. The core technology in deep learning is developed by image classification tasks. In the medical field, radiologists are specialists in such tasks. Using clinical applications based on deep learning would, therefore, be expected to contribute to substantial improvements in radiology. By gaining a better understanding of the features of deep learning, radiologists could be expected to lead medical development.

DOI： 10.1007/s11604-018-0795-3

PubMed

Other URL： http://link.springer.com/content/pdf/10.1007/s11604-018-0795-3.pdf

DOI： 10.1016/j.jvir.2023.11.014

PubMed

急速な人工知能(AI)の進化により、AI技術の医用画像への応用が急速に広がりつつあり、なかでも画像変換技術が注目を集めている。画像変換技術は、異なる画像モダリティ間での変換やアーチファクトの除去など幅広い応用が可能であり、画像変換モデルを用いてMRI画像からPETを生成したり、アーチファクトのある画像からアーチファクトのない画像へ変換することで、診断精度の向上や治療計画の最適化へ貢献する可能性がある。本稿では、筆者らが発表した画像変換技術を神経放射線科領域に応用した、「MRIを用いた疑似メチオニンPETの生成」「ミスレジストレーションアーチファクトのないDSAの生成」の2論文について紹介した。前者の疑似メチオニンPETは、神経膠腫のグレード分類や予後予測に実際のメチオニンPETと同等の付加価値があることが示された。後者の疑似DSAは、ミスレジストレーションアーチファクトを完全に取り除くことが可能であることが示された。

放射線分野における人工知能(AI)応用の研究数は飛躍的に増加している一方で,それらの多くは画像診断分野のものである。多くの日本の大学においては,画像診断分野とインターベンショナルラジオロジー(以下,IVR)分野は表裏一体となり,共に成長してきた。AI応用を得意としたわれわれの研究室も,画像診断分野へのAI応用だけではなく,IVR領域へのAI応用を意識するようになり,ようやく次世代のモーションアーチファクト低減技術に関して,AIを応用する研究を2本発表できたので,それらを紹介したい。いずれも同じ技術(pix2pixという画像変換技術)の論文であり,それを頭部領域,腹部領域に応用した点で異なっている。(著者抄録)

CiNii Article

Other URL： https://search.jamas.or.jp/link/ui/2022056359

2019年9月17日、医薬品医療機器総合機構(PMDA)は、大阪市立大学大学院医学研究科放射線診断学・IVR学教室(以下、当教室)が共同開発したmagnetic resonance angiography(MRA)からの脳動脈瘤補助診断プログラム(EIRL Aneurysm)を、人工知能(artificial intelligence:AI)の中でも特にディープラーニングを用いた医療機器として、日本で初めて医療機器として承認した。これは、まさに医療分野におけるAI時代の幕開けといえる。本稿では、「AI時代の新医療」について、AIと人間の認知の差異の観点を軸にしつつ、当教室でのAI研究の実例を交えて考えていきたい。(著者抄録)

近年、AIの放射線分野への応用が進んでいる。画像診断支援分野におけるAI分野の研究を大別すると、分類、検出、セグメンテーション、画像処理の4つとなる。これらそれぞれの分野を筆者らの研究を交えつつ紹介した。さらに、ディープラーニングで最近注目されている技術、および、AIと医療者の認知の違いについての実験を紹介し、来たるAI時代の医療者の役割について考察した。

J-GLOBAL

2019年9月17日、医薬品医療機器総合機構(PMDA)は、大阪市立大学大学院医学研究科放射線診断学・IVR学教室(以下、当教室)が共同開発したmagnetic resonance angiography(MRA)からの脳動脈瘤補助診断プログラム(EIRL Aneurysm)を、人工知能(artificial intelligence:AI)の中でも特にディープラーニングを用いた医療機器として、日本で初めて医療機器として承認した。これは、まさに医療分野におけるAI時代の幕開けといえる。本稿では、「AI時代の新医療」について、AIと人間の認知の差異の観点を軸にしつつ、当教室でのAI研究の実例を交えて考えていきたい。(著者抄録)

J-GLOBAL

J-GLOBAL

Publishing type：Research paper, summary (national, other academic conference)  

J-GLOBAL

J-GLOBAL

Presentation type：Poster presentation  

Presentation type：Oral presentation (invited, special)  

Presentation type：Oral presentation (invited, special)  

Presentation type：Oral presentation (invited, special)  

Presentation type：Oral presentation (invited, special)  

Presentation type：Oral presentation (invited, special)  

Presentation type：Oral presentation (keynote)  

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Presentation type：Public lecture, seminar, tutorial, course, or other speech