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Background and Aims: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs., Methods: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists., Results: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%., Conclusions: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Medical professionals are increasingly required to use digital technologies as part of care delivery and this may represent a risk for medical error and subsequent malpractice liability. For example, if there is a medical error, should the error be attributed to the clinician or the artificial intelligence-based clinical decision-making system? In this article, we identify and discuss digital health technology-specific risks for malpractice liability and offer practical advice for the mitigation of malpractice risk., (© 2022. The Author(s).)

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient's optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool-risk groups developed by the National Cancer Center Network (NCCN)-our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization., (© 2022. The Author(s).)

Radiology education is understood to be an important component of medical school and resident training, yet lacks a standardization of instruction. The lack of uniformity in both how radiology is taught and learned has afforded opportunities for new technologies to intervene. Now with the integration of artificial intelligence within medicine, it is likely that the current medical trainee curricula will experience the impact it has to offer both for education and medical practice. In this paper, we seek to investigate the landscape of radiologic education within the current medical trainee curricula, and also to understand how artificial intelligence may potentially impact the current and future radiologic education model., (Copyright © 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and "learning" patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most., Competing Interests: Conflict of Interest There are no conflicts of interest., (Thieme. All rights reserved.)

Background and Purpose: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification., Materials and Methods: The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons ( n = 122), medulloblastoma ( n = 272), pilocytic astrocytoma ( n = 135), and ependymoma ( n = 88). There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRIs as input to detect the presence of tumor and predict tumor class. Deep learning model performance was compared against that of 4 radiologists., Results: Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F 1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate. Tumor type classification accuracy and F 1 score were higher than those of 2 of the 4 radiologists., Conclusions: We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis., (© 2020 by American Journal of Neuroradiology.)

Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models. Our work demonstrates that both the swarm-based technology and deep-learning technology achieved superior diagnostic accuracy than the human experts alone. Our work further demonstrates that when used in combination, the swarm-based technology and deep-learning technology outperformed either method alone. The superior diagnostic accuracy of the combined HITL AI solution compared to radiologists and AI alone has broad implications for the surging clinical AI deployment and implementation strategies in future practice., Competing Interests: Competing interestsThe authors had control of the data and the information submitted for publication. Four authors (L.R., D.B., G.W. and M.L.) are employees of Unanimous AI, who developed the swarm platform used in this study. All other authors are not employees of or consultants for Unanimous AI and had control of the study methodology, data analysis, and results. There was no industry support specifically for this study. This study was supported in part by NSF through Award ID 1840937., (© The Author(s) 2019.)

Background and Purpose: Imaging self-referral is increasingly cited as a contributor to diagnostic imaging overuse. The purpose of this study was to determine whether ownership of MR imaging equipment by ordering physicians influences the frequency of negative cervical spine MR imaging findings., Materials and Methods: A retrospective review was performed of 500 consecutive cervical spine MRIs ordered by 2 separate referring-physician groups serving the same geographic community. The first group owned the scanners used and received technical fees for their use, while the second group did not. Final reports were reviewed, and for each group, the percentage of negative study findings and the frequency of abnormalities were calculated. The number of concomitant shoulder MRIs was recorded., Results: Five hundred MRIs meeting inclusion criteria were reviewed (250 with financial interest, 250 with no financial interest). Three hundred fifty-two had negative findings (190 with financial interest, 162 with no financial interest); there were 17.3% more scans with negative findings in the financial interest group (P = .006). Among scans with positive findings, there was no significant difference in the mean number of lesions per scan, controlled for age (1.90 with financial interest, 2.19 with no financial interest; P = .23). Patients in the financial interest group were more likely to undergo concomitant shoulder MR imaging (24 with financial interest, 11 with no financial interest; P = .02)., Conclusions: Cervical spine MRIs referred by physicians with a financial interest in the imaging equipment used were significantly more likely to have negative findings. There was otherwise a highly similar distribution and severity of disease between the 2 patient samples. Patients in the financial interest group were more likely to undergo concomitant shoulder MR imaging., (© 2014 by American Journal of Neuroradiology.)

Recent advances in imaging technology have created new opportunities for medical imaging to improve health care in resource-restricted countries around the world. Radiology residents are increasingly interested in global health and imaging outreach, yet infrastructure and opportunities for international outreach are limited. With the recent change in the ABR exam schedule, residents now have more flexibility in the fourth year of training to pursue elective interests, including participation in global health projects. Creating a formalized global health imaging curriculum will improve the quality, quantity, and overall impact of initiatives undertaken by residents and their training programs. A curriculum is proposed that provides content, opportunities for global health project development, and established metrics for effective evaluation and assessment. Four components considered integral to a global health imaging curriculum are described: (1) global and public health education; (2) targeted travel medicine education; (3) basic imaging proficiency; and (4) practice attitudes and accountability. Methods are presented of differentiating curricula to increase applicability across the spectrum of training programs that vary in available resources. A blueprint is presented for formalizing a global health curriculum or elective rotation within a program, as well as a resource for residents, radiologists, and organizations to make a meaningful impact on global health., (Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

A study of the fiber type composition of fourteen muscles spanning the human glenohumeral joint was carried out with the purpose of determining the contribution of fiber types to overall muscle cross-sectional area (CSA) and to estimate the maximum shortening velocity (V(max)) of those muscles. Muscle biopsies were procured from 4 male cadavers (mean age 50) within 24 hr of death, snap frozen, mounted, and transversely sectioned (10 microm). Slides were stained for myofibrillar ATPase after alkaline preincubation. Photoimages were taken of defined areas (100 fibers) using the Bioquant system, and fiber type and CSA were measured from these images. Staining for mATPase produced three different fiber types: slow-oxidative (SO), fast-oxidative-glycolytic (FOG), and fast-glycolytic (FG). On average, the muscle fiber type composition ranged from 22 to 40% of FG, from 17 to 51% of FOG, and from 23 to 56% of SO. Twelve out of the 14 muscles had average SO proportions ranging from 35 to 50%. V(max) was calculated from the fiber type contribution relative to CSA and shortening velocity values taken from the literature. The maximum velocities of shortening presented here provide a physiological basis for the development of human shoulder musculoskeletal models suitable for predicting muscle forces for functionally relevant tasks encompassing conditions of muscle shortening and lengthening.