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Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across image sequences to classify knee abnormalities. To assess the effectiveness of our model, we collected the largest multi-sequence knee magnetic resonance imaging dataset involving the most comprehensive range of abnormalities, comprising 1748 subjects and 12 types of abnormalities. Our model achieved an overall area under the receiver operating characteristic curve score of 0.812. It achieved an average accuracy of 0.78, outperforming junior radiologists (accuracy 0.65) and remains competitive with senior radiologists (accuracy 0.80). Notably, with the assistance of model output, the diagnosis accuracy of all radiologists was improved significantly (p < 0.001), elevating from 0.73 to 0.79 on average. The interpretability analysis demonstrated that the model decision-making process is consistent with the clinical knowledge, enhancing its credibility and reliability in clinical practice.

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The aim of the study was to investigate radiation protection adherence among radiology personnel and associated factors. In light of the increasing integration of ionizing radiation in medical diagnostics and treatment-specifically in areas such as computed tomography, fluoroscopy, and therapeutic radiology-it is vital for radiology personnel to consistently uphold rigorous radiation protection standards. This cross-sectional study employed a self-administered questionnaire to collect demographic data and assess various aspects of radiation protection adherence among radiology personnel. The gathered data were entered into SPSS 16 for statistical analysis. Among the 119 participants, 72 (60.5%) worked in the radiology department, and 88 (77.9%) were married. Significant associations were observed between adherence levels and marital status, age groups, years of experience, and department type. Study findings showed a significant association between several demographic factors and radiation protection adherence. Furthermore, our results highlight the value of implementing radiation protection courses to enhance adherence among personnel.

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AIM: To investigate the associations between the hour of the day and Prostate Imaging-Reporting and Data System (PI-RADS) scores assigned by radiologists in prostate MRI reports. MATERIALS AND METHODS: Retrospective single-center collection of prostate MRI reports over an 8-year period. Mean PI-RADS scores assigned between 0800 and 1800 h were examined with a regression model. RESULTS: A total of 35'004 prostate MRI interpretations by 26 radiologists were included. A significant association between the hour of day and mean PI-RADS score was identified (ß2 = 0.005, p < 0.001), with malignant scores more frequently assigned later in the day. CONCLUSION: These findings suggest chronobiological factors may contribute to variability in radiological assessments. Though the magnitude of the effect is small, this may potentially add variability and impact diagnostic accuracy.

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Inclusive leadership styles value team members, invite diverse perspectives, and recognize and support the contributions of employees. The authors provide guidance to radiology leaders interested in developing inclusive leadership skills and competencies to improve workforce recruitment and retention and unlock the potential of a rapidly diversifying health care workforce. As health care organizations look to attract the best and brightest talent, they will be increasingly recruiting millennial and Generation Z employees, who belong to the most diverse generations in American history. Additionally, radiology departments currently face critical workforce shortages in radiologists, radiology technicians, staff, and advanced practice providers. In the context of these shortages, the costs of employee turnover have emphasized the need for radiology leaders to develop leadership behaviors that promote recruitment and retention. Radiology department leaders who perceive and treat valued employees as replaceable commodities will be forced to deal with the extremely high costs associated with recruitment and training, decreased morale, and increased burnout. The authors review inclusive versus exclusive leadership styles, describe key attributes and skills of inclusive leaders, provide radiology leaders with concrete methods to make their organizations more inclusive, and outline key steps in change management. By adopting and implementing inclusive leadership strategies, radiology groups can position themselves to succeed in rapidly diversifying health care environments. ©RSNA, 2024 See the invited commentary by Siewert in this issue.

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OBJECTIVE: This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden. METHODS: We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis. RESULTS: We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one. CONCLUSION: The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.

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BACKGROUND: Industry payments to physicians are common, but it is unknown how the payments in different categories to radiologists compare to other specialties. OBJECTIVE: The aim of this study is to assess the proportion of industry payments to physicians in radiology in certain categories relative to other specialties. METHODS: The Open Payments Database was analyzed from January 1, 2017 to December 31, 2021 for industry payments to all allopathic & osteopathic physicians, and classified into distinct clinical specialties. Payments to physicians in three categories were calculated in relation to total payments in each specialty during the study period: consulting fees, research, and royalties/ownership (royalty, license, or current or prospective ownership or investment). RESULTS: The total value of industry payments to physicians across all specialties was just under $13 billion over the six-year period from 2017 to 2022. During this period, 51.4 million total payments were made to 791,746 physicians. US physicians in radiology received 452,027 payments for a total value of $357 million (2.8 % of total value). For radiologists, 32.8 % of industry payment value was attributed to royalties/ownership and 9.9 % to research, collectively adding up to 42.7 % of all industry payment. The only specialties with higher payments in these two categories considered reflective of innovation payments were the surgical specialties with higher royalty payments. CONCLUSION: The proportion of industry payments in radiology in categories reflecting innovation (royalty/ownership and research fees) is high and second only to surgical specialties.

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INTRODUCTION: Global education, particularly Continuing Medical Education (CME) for healthcare professionals, is quickly shifting online. This study assesses the opportunities and challenges of adopting online learning in radiology CME. It explores how radiologists and radiographers have adapted to this digital shift and the changing landscape of radiology education. The study also seeks to envision an innovative future for radiology education. METHODS: A descriptive cross-sectional survey was conducted among radiologists and radiographers working in radiology departments in the United Arab Emirates (UAE). The survey collected data on participant demographics, experiences with CME, sources of CME, and perceptions of online learning. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) software. RESULTS: The survey involved 65 radiologists and 215 radiographers. Findings indicated a significant shift from face-to-face to online CME activities, with 76.9% of radiologists and 70.7% of radiographers utilizing online resources for CME. Concerns about time management, technical issues, and expenses have emerged as challenges for online CME. Participants also highlighted the importance of free-of-charge CME and the value of active participation and anonymity in online discussions. CONCLUSION: Radiology professionals have rapidly adapted to the changing landscape of CME by embracing online learning. While this shift offers greater flexibility and accessibility, technology-related challenges and concerns over time management persist. The study suggests that the future of radiology CME may involve personalized, adaptive, and interactive learning experiences, emphasizing mental well-being and resilience. IMPLICATIONS FOR PRACTICE: Radiology professionals must embrace online CME for continuous skill enhancement, addressing technical challenges, fostering interactive learning environments, and ensuring accessibility to maintain high standards in patient care and medical advancements.

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Imaging-based screening is an important public health focus and a fundamental part of Diagnostic Radiology. Hence, radiologists should be familiar with the concepts that drive imaging-based screening practice including goals, risks, biases and clinical trials. This review article discusses an array of imaging-based screening exams including the key epidemiology and evidence that drive screening guidelines for abdominal aortic aneurysm, breast cancer, carotid artery disease, colorectal cancer, coronary artery disease, lung cancer, osteoporosis, and thyroid cancer. We will provide an overview on societal interests in screening, screening-related inequities, and opportunities to address them. Emerging evidence for opportunistic screening and the role of AI in imaging-based screening will be explored. In-depth knowledge and formalized training in imaging-based screening strengthens radiologists as clinician scientists and has the potential to broaden our public health leadership opportunities. SUMMARY SENTENCE: An overview of key screening concepts, the evidence that drives today's imaging-based screening practices, and the need for radiologist leadership in screening policies and evidence development.

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For better collaboration among radiologists, the interpretation workload should be evaluated, considering the difference in difficulty for interpreting each case. However, objective evaluation of difficulty is challenging. This study proposes a multimodal classifier of structural and textual data to predict difficulty based on order information and patient data without using images. The classifier showed performance with a specificity of 0.9 and an accuracy of 0.7.

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ABSTRACT: The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

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The average post-pandemic modern radiology practice is experiencing an ever-increasing workload volume with overall relatively similar staffing levels, regardless of practice setting. This has resulted in an increased workload demand for the average diagnostic radiologist, which in many cases translates to longer working hours. It is now more important than ever to be cognizant of various work-related injuries, including repetitive-stress injuries and vision-related ailments as examples, in relation to the working conditions of the radiologist. This article will discuss commonly occurring conditions and ergonomic considerations that the radiologist can employ to reduce the risk of work-related injuries.

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BACKGROUND: This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. METHODS: In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. RESULTS: According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). CONCLUSIONS: The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

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