用新模型自动识别髋关节发育不良，准确率比传统方法高5个百分点。
Gaussian Process Diffeomorphic Statistical Shape Modelling Outperforms Angle-Based Methods for Assessment of Hip Dysplasia
- 融合高斯过程与微分同胚构建统计形貌模型
- 在192例数据上达到96.2%的分类准确率（AUC）
- 无需手动量角，适合临床医生快速筛查
髋关节发育不良是髋骨性关节炎的重要风险因素，早期诊断可为手术干预提供机会。本文基于患者髋部的三维CT扫描和少量临床标注点，提出一种半自动化发育不良分类流程，结合高斯过程潜在变量模型与微分同胚思想，构建了高斯过程微分同胚统计形貌模型（GPDSSM）。使用192例CT数据，其中100例用于模型训练，92例用于测试。GPDSSM能有效区分发育不良样本与正常对照，并揭示表面异常区域。相比角度测量法，其分类性能更优（AUC 96.2% vs 91.2%），同时可免除临床医生手动测量角度及解读二维影像的繁琐操作，显著提升效率。
原文摘要 · Abstract (English)
Dysplasia is a recognised risk factor for osteoarthritis (OA) of the hip, early diagnosis of dysplasia is important to provide opportunities for surgical interventions aimed at reducing the risk of hip OA. We have developed a pipeline for semi-automated classification of dysplasia using volumetric CT scans of patients' hips and a minimal set of clinically annotated landmarks, combining the framework of the Gaussian Process Latent Variable Model with diffeomorphism to create a statistical shape model, which we termed the Gaussian Process Diffeomorphic Statistical Shape Model (GPDSSM). We used 192 CT scans, 100 for model training and 92 for testing. The GPDSSM effectively distinguishes dysplastic samples from controls while also highlighting regions of the underlying surface that show dysplastic variations. As well as improving classification accuracy compared to angle-based methods (AUC 96.2% vs 91.2%), the GPDSSM can save time for clinicians by removing the need to manually measure angles and interpreting 2D scans for possible markers of dysplasia.
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