MUSCULOSKELETAL IMAGINGF racture detection using radiography is one of the most common tasks in patients with high-or low-energy trauma in various clinical settings, including the emergency department, urgent care, and outpatient clinics such as orthopedics, rheumatology, and family medicine. Missed fractures on radiographs are one of the most common causes of diagnostic discrepancies between initial interpretations by nonradiologists or radiology residents and the final read by board-certified radiologists, leading to preventable harm or delay in care to the patient (1-3). Fracture interpretation errors can represent up to 24% of harmful diagnostic errors seen in the emergency department (2). Furthermore, inconsistencies in radiographic diagnosis of fractures are more common during the evening and overnight hours (5 pm to 3 am), likely related to nonexpert reading and fatigue (3). In patients with multiple traumas, the proportion of missed injuries, including fractures, can be high on the forearm and hands (6.6%) and feet (6.5%) (4,5).To date, several studies about artificial intelligence (AI) aid to fracture detection have been performed focusing only on certain body parts, such as hand, wrist, and forearm (6-9); hip and pelvis (10,11); knees (9); and spine (12). One study evaluated fractures in 11 body locations, Background: Missed fractures are a common cause of diagnostic discrepancy between initial radiographic interpretation and the final read by board-certified radiologists.Purpose: To assess the effect of assistance by artificial intelligence (AI) on diagnostic performances of physicians for fractures on radiographs. Materials and Methods:This retrospective diagnostic study used the multi-reader, multi-case methodology based on an external multicenter data set of 480 examinations with at least 60 examinations per body region (foot and ankle, knee and leg, hip and pelvis, hand and wrist, elbow and arm, shoulder and clavicle, rib cage, and thoracolumbar spine) between July 2020 and January 2021. Fracture prevalence was set at 50%. The ground truth was determined by two musculoskeletal radiologists, with discrepancies solved by a third. Twenty-four readers (radiologists, orthopedists, emergency physicians, physician assistants, rheumatologists, family physicians) were presented the whole validation data set (n = 480), with and without AI assistance, with a 1-month minimum washout period. The primary analysis had to demonstrate superiority of sensitivity per patient and the noninferiority of specificity per patient at 23% margin with AI aid. Stand-alone AI performance was also assessed using receiver operating characteristic curves.Results: A total of 480 patients were included (mean age, 59 years 6 16 [standard deviation]; 327 women). The sensitivity per patient was 10.4% higher (95% CI: 6.9, 13.9; P , .001 for superiority) with AI aid (4331 of 5760 readings, 75.2%) than without AI (3732 of 5760 readings, 64.8%). The specificity per patient with AI aid (5504 of 5760 readings, 95.6%) was noninferior to that ...

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. Key Points • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

T raumatic skeletal injuries are a leading source of consultation in emergency departments, with an annual incidence reported to be as high as 1.3% in the United States (1) and 0.32% in China (2). Radiography is the first-line imaging modality for the diagnosis of these lesions and the most used imaging modality worldwide (3-5). The reading of trauma radiographs is a demanding task that requires radiologic expertise, and there is a lack of radiologists (6). Consequently, emergency physicians are required to make patient treatment decisions before the availability of a radiologist's report, with a risk of interpretation error (7-9). Missed fractures, a preventable cause of morbidity (10), represent up to 80% of emergency department diagnostic errors (11). In American medical-legal claims, extremity fractures are the second most frequently missed diagnosis leading to a claim, after breast cancer (12). Assisting physicians in detecting and localizing fractures on plain radiographs could therefore reduce error rates.Computer-aided detection software has been developed for more than 20 years to provide decision support to radiologists, especially for screening breast cancer on mammograms (13) and lung nodules on CT scans (14). However, computer-aided detection systems have a high false-positive rate, which has limited their acceptance (13). Similar technologies have been unsuccessfully investigated for fracture detection, potentially because of Background: The interpretation of radiographs suffers from an ever-increasing workload in emergency and radiology departments, while missed fractures represent up to 80% of diagnostic errors in the emergency department.Purpose: To assess the performance of an artificial intelligence (AI) system designed to aid radiologists and emergency physicians in the detection and localization of appendicular skeletal fractures. Materials and Methods:The AI system was previously trained on 60 170 radiographs obtained in patients with trauma. The radiographs were randomly split into 70% training, 10% validation, and 20% test sets. Between 2016 and 2018, 600 adult patients in whom multiview radiographs had been obtained after a recent trauma, with or without one or more fractures of shoulder, arm, hand, pelvis, leg, and foot, were retrospectively included from 17 French medical centers. Radiographs with quality precluding human interpretation or containing only obvious fractures were excluded. Six radiologists and six emergency physicians were asked to detect and localize fractures with (n = 300) and fractures without (n = 300) the aid of software highlighting boxes around AIdetected fractures. Aided and unaided sensitivity, specificity, and reading times were compared by means of paired Student t tests after averaging of performances of each reader.Results: A total of 600 patients (mean age 6 standard deviation, 57 years 6 22; 358 women) were included. The AI aid improved the sensitivity of physicians by 8.7% (95% CI: 3.1, 14.2; P = .003 for superiority) and the specificity by 4.1% (95% CI: ...

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