Global Attention Mechanism–Enhanced Deep Learning for Multi‐Sequence MRI Diagnosis of Early Femoral Head Osteonecrosis

ABSTRACT

Accurate diagnosis of early‐stage osteonecrosis of the femoral head (ONFH) remains challenging due to reliance on subjective radiological interpretation. This multicenter retrospective study developed an automated, multi‐sequence MRI‐based diagnostic model for early ONFH using data from 342 ONFH‐affected femoral heads (FHs) from 282 patients and 265 healthy FHs from 233 healthy individuals obtained from two geographically distinct institutions. The dataset comprised three MRI sequences (T1WI, FS‐T2WI, and Cor STIR) and 7844 annotated FH instances labeled as normal or abnormal. A You Only Look Once (YOLO) segmentation model was trained on both single‐sequence and multi‐sequence datasets, and a global attention mechanism (GAM) was integrated to enhance diagnostic performance, yielding the GAM‐YOLO model. Model performance was compared with that of radiology residents using Fisher's exact test. The multi‐sequence YOLO model achieved superior diagnostic accuracy (sensitivity 93.19%, specificity 94.65%, accuracy 94.04%) compared to single‐sequence models. Incorporation of GAM further improved performance (sensitivity 97.00%, specificity 97.52%, accuracy 97.30%; p < 0.05). When applied to entire FH images, the GAM‐YOLO model achieved sensitivity, specificity, and accuracy of 99.03%, 98.51%, and 98.82%, respectively, showing higher diagnostic performance than residents ( p < 0.01). These findings suggest that the GAM‐YOLO model offers a promising and objective tool for early ONFH diagnosis, demonstrating potential for clinical application.