基于面部表型的罕见病诊断：使用2D和3D摄影对Stickler综合征的定量评估

文献信息

中文摘要

Stickler综合征（SS）是一种由胶原蛋白编码基因突变引起的罕见遗传性疾病。SS患者发生视网膜脱离的风险增加，眼科并发症常因显著的诊断延迟而成为首发症状。这些延迟导致严重的视力损害，甚至在儿童早期即可出现，使诊断不确定性成为主要的临床挑战。本研究评估了利用面部特征进行SS早期检测的可行性。研究将监督机器学习模型（包括XGBoost和支持向量机SVM）应用于2D和3D面部照片，以区分SS个体与健康对照。模型使用全脸2D和3D照片识别SS患者的准确率达到92%。当专门关注3D图像中的眶鼻区域时，分类准确率提高至96%。Stickler综合征等罕见病的面部形态可作为高度准确、非侵入性的筛查工具。基于AI的分析有可能减少诊断延迟并预防相关并发症，从而改善患者预后。

英文摘要

Stickler syndrome (SS) is a rare genetic disorder caused by mutations in collagen-encoding genes. Patients with SS are at an increased risk of retinal detachment, with ophthalmologic complications often presenting as the first symptom due to significant diagnostic delays. These delays contribute to severe visual impairment, even in early childhood, making diagnostic uncertainty a major clinical challenge. This study assessed the feasibility of early SS detection using facial features. Supervised machine learning models, including XGBoost and support vector machines (SVM), were applied to 2D and 3D facial photographs to differentiate individuals with SS from healthy controls. The models achieved 92% accuracy in identifying SS patients using full-face 2D and 3D photographs. When focusing specifically on the orbitonasal region in 3D images, classification accuracy increased to 96%. Facial morphology in rare diseases such as Stickler syndrome can serve as a highly accurate, non-invasive screening tool. AI-based analysis has the potential to reduce diagnostic delays and prevent associated complications, improving patient outcomes.