Deep Learning-Based Automatic Glenohumeral Joint Segmentation for Determining Whether the Hill-Sachs Lesion Is On-Track or Off-Track.

Background: Accurate quantification of the glenoid track is critical for determining optimal treatment strategies in patients with anterior shoulder instability. However, conventional computed tomography (CT)-based assessment methods require approximately 2 hours of manual segmentation and suffer from limited interobserver consistency, which may compromise diagnostic accuracy and surgical planning. Deep learning has demonstrated significant potential in medical image analysis. Purpose: To propose a deep learning-based framework for automated CT segmentation and bone defect quantification in anterior shoulder dislocation to enhance diagnostic efficiency and consistency. Study Design: Cohort Study (diagnosis); Level of evidence, 3. Methods: A deep learning model was developed by adapting the TotalSegmentator framework to perform automated segmentation and 3-dimensional (3D) reconstruction of CT images from 43 patients with anterior shoulder dislocation. Glenoid track width (GTW) and Hill-Sachs interval (HSI) were manually assessed using the Two-Thirds Glenoid Height Technique by 1 senior shoulder and elbow surgeon and 2 junior physicians. Semi-automated determination of the on-track/off-track status was also performed. Segmentation performance and measurement method reliability were evaluated using the Dice similarity coefficient and intraclass correlation coefficient (ICC), respectively. Results: The model achieved excellent segmentation accuracy, with mean Dice similarity coefficients exceeding 0.95 for both the scapula and humerus. Segmentation time was significantly reduced compared with manual segmentation, requiring only 30 seconds per case. Based on the segmented images, the GTW measured using the Two-Thirds Glenoid Height Technique demonstrated almost perfect intra- and interobserver agreement (ICC > 0.90). HSI measurements showed almost perfect intraobserver reliability (ICC > 0.90) and substantial interobserver agreement (ICC ≥ 0.80). The semi-automated determination of on-track/off-track status improved workflow efficiency, saving approximately 2 hours compared with the fully manual approach. Conclusion: This study integrates deep learning techniques into the entire diagnostic workflow for shoulder dislocation, enabling rapid, accurate quantification of bony defects. The reliability of using the Two-Thirds Glenoid Height Technique for measuring glenoid parameters on 3D models was validated, offering an efficient tool for surgical planning.