Deep learning-enabled monitoring of postoperative fracture healing on serial radiographs: a 150-patient study using an enhanced YOLOv11 framework.

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Abstract

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. 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. 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. 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.

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