Musculoskeletal Computed Tomography: How to Add Value When Reporting Adult Upper Limb Trauma.

ABSTRACT: There is increasing reliance on computed tomography to evaluate fractures and dislocations following routine evaluation with plain radiography, critical in preoperative planning; computed tomography can provide multiplanar reformats and 3-dimensional volume-rendered imaging, providing a better global assessment for the orthopedic surgeon. The radiologist plays a critical role in appropriately reformatting the raw axial images to illustrate best the findings that will help determine further management. In addition, the radiologist must succinctly report the pertinent findings that will have the most significant bearing on treatment, assisting the surgeon in deciding between nonoperative and operative management. The radiologist should also carefully review imaging to look for ancillary findings in the setting of trauma beyond the bones and joints, including the lungs and rib cage when visualized.In this review article, we will systematically describe key features for fractures of the scapula, proximal humerus, distal humerus, radial head and neck, olecranon, coronoid process through a case-based approach, and distal radius. Although there are numerous detailed classification systems for each of these fractures, we aim to focus on the core descriptors that underpin these classification systems. The goal is to provide the radiologist with a checklist of critical structures they must assess and findings that they should mention in their report, emphasizing those descriptors that influence patient management.

Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study.

OBJECTIVE: To determine whether an artificial intelligence candidate could pass the rapid (radiographic) reporting component of the Fellowship of the Royal College of Radiologists (FRCR) examination. DESIGN: Prospective multi-reader diagnostic accuracy study. SETTING: United Kingdom. PARTICIPANTS: One artificial intelligence candidate (Smarturgences, Milvue) and 26 radiologists who had passed the FRCR examination in the preceding 12 months. MAIN OUTCOME MEASURES: Accuracy and pass rate of the artificial intelligence compared with radiologists across 10 mock FRCR rapid reporting examinations (each examination containing 30 radiographs, requiring 90% accuracy rate to pass). RESULTS: When non-interpretable images were excluded from the analysis, the artificial intelligence candidate achieved an average overall accuracy of 79.5% (95% confidence interval 74.1% to 84.3%) and passed two of 10 mock FRCR examinations. The average radiologist achieved an average accuracy of 84.8% (76.1-91.9%) and passed four of 10 mock examinations. The sensitivity for the artificial intelligence was 83.6% (95% confidence interval 76.2% to 89.4%) and the specificity was 75.2% (66.7% to 82.5%), compared with summary estimates across all radiologists of 84.1% (81.0% to 87.0%) and 87.3% (85.0% to 89.3%). Across 148/300 radiographs that were correctly interpreted by >90% of radiologists, the artificial intelligence candidate was incorrect in 14/148 (9%). In 20/300 radiographs that most (>50%) radiologists interpreted incorrectly, the artificial intelligence candidate was correct in 10/20 (50%). Most imaging pitfalls related to interpretation of musculoskeletal rather than chest radiographs. CONCLUSIONS: When special dispensation for the artificial intelligence candidate was provided (that is, exclusion of non-interpretable images), the artificial intelligence candidate was able to pass two of 10 mock examinations. Potential exists for the artificial intelligence candidate to improve its radiographic interpretation skills by focusing on musculoskeletal cases and learning to interpret radiographs of the axial skeleton and abdomen that are currently considered "non-interpretable."