Radiography-based AI decision support for further post-traumatic knee MRI referral in children.

PURPOSE: The aim of our systematic work was to investigate an artificial intelligence-based prediction of relevant internal injuries of the paediatric knee joint based on initial radiographs and to develop a corresponding AI model. METHODS: We queried the hospital information systems of two independent sites for pediatric and adolescent patients up to the age of 19 years with a history of recent trauma, who had undergone a radiograph at ap and lateral projections and Magnetic Resonance Imaging (MRI) of the same knee joint within 48 hours. After exclusion of patients due to postoperative situations, tumorous or infectious diseases, missing and invalid radiographs, 873 patients with 1746 total images were included. Each model was assessed for precision, recall, accuracy and the F1 score, revealing variation between model versions in performance metrics. RESULTS: The averaged performance across all EfficientNet models achieved a precision of 0.7340, recall of 0.7181, accuracy of 0.7131, and 0.7260. The best per- forming model, EfficientNet-B5, achieved an average precision of 0.7445, recall for 0.7890, and accuracy for 0.7450, respectively. The heat maps revealed significant concentrations of pathologies detected by AI primarily in the femoral and tibial condyles. AI also identified fractures and microtrabecular fractures, suggesting their effectiveness in identifying relevant injuries on (pediatric) knee radiograph.