Automated glenoid bone loss measurement and segmentation in CT scans for pre-operative planning in shoulder instability

2025-11-17

AI Summary

Preoperative planning for shoulder instability remains hindered by time-consuming, inter-observer-variable manual or semi-automatic quantification of glenoid bone loss. This work introduces the first end-to-end deep learning framework for fully automated 3D segmentation and percentage quantification of glenoid bone defects from CT scans, integrating U-Net–based 3D segmentation, anatomical keypoint detection, and geometric fitting (PCA-based alignment, orthogonal projection, and circular fitting). Validated on multicenter data, the method achieves excellent agreement with expert consensus (ICC = 0.84), surpassing inter-rater reliability among clinicians (ICC = 0.78). Subgroup classification accuracy for high- versus low-grade defects ranges from 71.4% to 85.7%, with no severe misclassifications. To our knowledge, this is the first clinically viable, fully automated 3D bone loss analysis pipeline, delivering a reliable, reproducible imaging biomarker to support personalized surgical planning.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reliable measurement of glenoid bone loss is essential for operative planning in shoulder instability, but current manual and semi-automated methods are time-consuming and often subject to interreader variability. We developed and validated a fully automated deep learning pipeline for measuring glenoid bone loss on three-dimensional computed tomography (CT) scans using a linear-based, en-face view, best-circle method. Shoulder CT images of 91 patients (average age, 40 years; range, 14-89 years; 65 men) were retrospectively collected along with manual labels including glenoid segmentation, landmarks, and bone loss measurements. The multi-stage algorithm has three main stages: (1) segmentation, where we developed a U-Net to automatically segment the glenoid and humerus; (2) anatomical landmark detection, where a second network predicts glenoid rim points; and (3) geometric fitting, where we applied principal component analysis (PCA), projection, and circle fitting to compute the percentage of bone loss. The automated measurements showed strong agreement with consensus readings and exceeded surgeon-to-surgeon consistency (intraclass correlation coefficient (ICC) 0.84 vs 0.78), including in low- and high-bone-loss subgroups (ICC 0.71 vs 0.63 and 0.83 vs 0.21, respectively; P < 0.001). For classifying patients into low, medium, and high bone-loss categories, the pipeline achieved a recall of 0.714 for low and 0.857 for high severity, with no low cases misclassified as high or vice versa. These results suggest that our method is a time-efficient and clinically reliable tool for preoperative planning in shoulder instability and for screening patients with substantial glenoid bone loss. Code and dataset are available at https://github.com/Edenliu1/Auto-Glenoid-Measurement-DL-Pipeline.

Problem

• Automating glenoid bone loss measurement in CT scans for shoulder instability planning
• Reducing time-consuming manual methods with high interreader variability
• Developing reliable deep learning pipeline for preoperative surgical planning

Innovation

• U-Net segmentation for glenoid and humerus
• Neural network predicts glenoid rim landmarks
• PCA and circle fitting compute bone loss