Anatomical Alignment of Femoral Radiographs Enables Robust AI-Powered Detection of Incomplete Atypical Femoral Fractures

An Incomplete Atypical femoral fracture is subtle and requires early diagnosis. However, artificial intelligence models for these fractures often fail in real-world clinical settings due to the “domain shift” problem, where performance degrades when applied to new data sources. This study proposes a data-centric approach to overcome this problem. We introduce an anatomy-based four-step preprocessing pipeline to normalize femoral X-ray images. This pipeline consists of (1) semantic segmentation of the femur, (2) skeletonization and centroid extraction using RANSAC, (3) rotational alignment to the vertical direction, and (4) cropping a normalized region of interest (ROI). We evaluate the effectiveness of this pipeline across various one-stage (YOLO) and two-stage (Faster R-CNN) object detection models. On the source domain data, the proposed alignment pipeline significantly improves the performance of the YOLO model, with YOLOv10n achieving the best performance of 0.6472 at mAP@50–95. More importantly, in zero-shot evaluation on a completely new domain, standing AP X-ray, the model trained on aligned data exhibited strong generalization performance, while the existing models completely failed (mAP = 0), YOLOv10s, which applied the proposed method, achieved 0.4616 at mAP@50–95. The first-stage detector showed more consistent performance gains from the alignment technique than the second-stage detector. Normalizing medical images based on inherent anatomical consistency is a highly effective and efficient strategy for achieving domain generalization. This data-driven paradigm, which simplifies the input to AI, can create clinically applicable, robust models without increasing the complexity of the model architecture.