Development of an XAI-Enhanced Deep-Learning Algorithm for Automated Decision-Making on Shoulder-Joint X-Ray Retaking

Abstract

Purpose: To develop and validate a two-stage system for automated quality assessment of shoulder true-AP radiographs by combining joint localization with quality classification. Materials and Methods: From the MURA “SHOULDER” subset, 2956 anteroposterior images were identified; 59 images with negative–positive inversion, excessive metallic implants, extreme exposure, or presumed fluoroscopy were excluded, yielding a class-balanced set of 2800 images (1400 OK/1400 NG). A YOLOX-based detector localized the glenohumeral joint, and classifiers operated on both whole images and detector-centered crops. To enhance interpretability, we integrated Grad-CAM into both whole-image and local classifiers and assessed attention patterns against radiographic criteria. Results: The detector achieved AP@0.5 = 1.00 and a mean Dice similarity coefficient of 0.967. The classifier attained AUC = 0.977 (F1 = 0.943) on a held-out test set. Heat map analyses indicated anatomically focused attention consistent with expert-defined regions, and coverage metrics favored local over whole-image models. Conclusions: The two-stage, XAI-integrated approach provides accurate and interpretable assessment of shoulder true-AP image quality, aligning model attention with radiographic criteria.

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