Abstract Objectives To develop and validate a deep learning framework for classifying postoperative time-points as a proxy task for monitoring longitudinal fracture healing progression from serial radiographs. Methods This retrospective study included 150 patients with paired pre-treatment and follow-up X-ray images. We built a detection-guided pipeline comprising (1) fracture-region localization using an enhanced YOLOv11 detector integrating attention mechanism, Focal-SIoU loss, and data augmentation, and (2) healing-status prediction from detected regions of interest by quantifying callus formation and fracture-line changes over time. Data were split at the patient level into training/validation/test cohorts. Performance was evaluated using accuracy, F1 score, ROC/AUC, and calibration, and compared with clinician readings. Results The YOLOv11–guided framework achieved reliable fracture localization and consistent healing assessment on serial radiographs. On the independent test set, it showed stable discriminative ability across follow-up stages and improved robustness over manual interpretation, particularly at early postoperative time points when radiographic changes are subtle. Conclusions This single-center study demonstrates a technical framework for objective and scalable radiograph-based longitudinal fracture-healing monitoring. External, multi-center validation is required before broader clinical deployment. The proposed detection-enhanced YOLOv11 framework may support clinical follow-up and decision-making after fracture surgery.
Teng et al. (2026) studied this question.