AI自动测量肩关节盂骨缺损，准确度超医生水平
Fully Automated Deep Learning Based Glenoid Bone Loss Measurement and Severity Stratification on 3D CT in Shoulder Instability
- 用U-Net分割骨骼，双网络定位盂缘点，几何拟合算骨缺损率
- 与专家共识一致，高缺损组一致性达ICC 0.83，优于医生间一致性
- 可区分高低严重程度，无误判，适合术前评估临床使用
为开发并验证一种全自动深度学习流程，基于3D CT扫描的线性法、正对视图和最佳圆法测量肩关节不稳患者的盂骨缺损。回顾性收集2013年1月至2023年3月期间81例患者肩部CT数据。算法分三阶段：(1) 分割——采用U-Net自动分割盂和肱骨头；(2) 解剖标志检测——第二网络预测盂缘点；(3) 几何拟合——通过主成分分析（PCA）、投影和圆拟合计算骨缺损百分比。使用骰数系数（DSC）评估分割性能，以平均绝对误差（MAE）和组内相关系数（ICC）评估骨缺损测量表现，同时评估中间输出（边缘点集和正对视图）。自动化测量与专家共识高度一致，优于外科医生间一致性（全部患者ICC 0.84 vs 0.78；低缺损组ICC 0.71 vs 0.63；高缺损组ICC 0.83 vs 0.21；P < 0.001）。在将患者分类至不同骨缺损严重程度亚组的任务中，对低严重程度组敏感度为71.4%，高严重程度组为85.7%，且无低误判为高或反之的情况。该全自动深度学习管道可用于临床可靠的肩关节不稳术前规划。模型与数据集已开源：https://github.com/Edenliu1/Auto-Glenoid-Measurement-DL-Pipeline。
原文摘要 · Abstract (English)
To develop and validate a fully automated, deep-learning pipeline for measuring glenoid bone loss on 3D CT scans using linear-based, en-face view, and best-circle method. Shoulder CT scans of 81 patients were retrospectively collected between January 2013 and March 2023. Our algorithm consists of 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 a principal component analysis (PCA), projection, and circle fitting to compute the percentage of bone loss. The performance of the pipeline was evaluated using DSC for segmentation and MAE and ICC for bone-loss measurement; intermediate outputs (rim point sets and en-face view) were also assessed. Automated measurements showed strong agreement with consensus readings, exceeding surgeon-to-surgeon consistency (ICC 0.84 vs 0.78 for all patients; ICC 0.71 vs 0.63 for low bone loss; ICC 0.83 vs 0.21 for high bone loss; P < 0.001). For the classification task of assigning each patient to different bone loss severity subgroups, the pipeline's sensitivity was 71.4% for the low-severity group and 85.7% for the high-severity group, with no instances of misclassifying low as high or vice versa. A fully automated, deep learning-based pipeline for glenoid bone-loss measurement on CT scans can be a clinically reliable tool to assist clinicians with preoperative planning for shoulder instability. We are releasing our model and dataset at https://github.com/Edenliu1/Auto-Glenoid-Measurement-DL-Pipeline .
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