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.”

Background/Objectives: Intraneural tumors (INTs) pose a diagnostic challenge, owing to their varied origins within nerve fascicles and their wide spectrum, which includes both benign and malignant forms. Accurate diagnosis and management of these tumors depends upon the skills of the radiologist in identifying key imaging features and correlating them with the patient’s clinical symptoms and examination findings. Methods: This comprehensive review systematically analyzes the various imaging features in the diagnosis of intraneural tumors, ranging from basic MR to advanced MR imaging techniques such as MR neurography (MRN), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) MRI. Results: The article emphasizes the differentiation of benign from malignant lesions using characteristic MRI features, such as the “target sign” and “split-fat sign” for tumor characterization. The role of advanced multiparametric MRI in improving biopsy planning, guiding surgical mapping, and enhancing post-treatment monitoring is also highlighted. The review also underlines the importance of common diagnostic pitfalls and highlights the need for a multi-disciplinary approach to achieve an accurate diagnosis, appropriate treatment strategy, and post-therapy surveillance planning. Conclusions: In this review, we illustrate the main imaging findings of intraneural tumors, focusing on specific MR imaging features that are crucial for an accurate diagnosis and the differentiation between benign and malignant lesions.

Guyon’s canal, or the ulnar tunnel, is a critical anatomical structure at the wrist that houses the ulnar nerve and artery, making it susceptible to various pathological conditions. Pathologies affecting this canal include traumatic injuries, compressive neuropathies like ulnar tunnel syndrome, and space-occupying lesions such as ganglion cysts. Ulnar tunnel syndrome, characterised by numbness, tingling, and weakness in the ulnar nerve distribution, is a prevalent condition that can severely impair hand function. The canal’s intricate anatomy is defined by surrounding ligaments and bones, divided into three zones, each containing distinct neural structures. Variations, including aberrant muscles and vascular anomalies, can complicate diagnosis and treatment. Imaging techniques are essential for evaluating these conditions; ultrasound provides real-time, dynamic assessments, while magnetic resonance imaging (MRI) offers detailed visualisation of soft tissues and bony structures, aiding in pre-surgical documentation and pathology evaluation. This review article explores the anatomy, pathologies, and imaging modalities associated with Guyon’s canal and underscores the necessity of understanding Guyon’s canal’s anatomy and associated pathologies to improve diagnostic accuracy and management strategies. By integrating anatomical insights with advanced imaging techniques, clinicians can enhance patient outcomes and preserve hand function, emphasising the need for increased awareness and research in this often-neglected area of hand anatomy.