Developing Facial and Speech Digital Tools for Clinical Assessments of Patients with Huntington's Disease: An Exploratory Study

Facial dyskinesia and speech abnormalities are prominent clinical features of Huntington’s disease (HD), yet their objective quantification remains limited in routine clinical practice. In this exploratory study, we employed a standardized Python-based digital assessment protocol to extract quantitative facial and speech features from individuals with manifest HD and healthy controls (HC).17 facial features across four facial regions and 42 speech features spanning fundamental frequency, loudness regulation, phonation instability, and temporal dynamics were analyzed.面部运动障碍与言语异常是亨廷顿舞蹈症（Huntington’s disease, HD）的显著临床特征，但在常规临床实践中，针对二者的客观量化手段仍较为有限。本探索性研究采用了基于Python的标准化数字化评估方案，从确诊亨廷顿舞蹈症患者与健康对照（healthy controls, HC）群体中提取量化的面部与言语特征。本次研究共分析了覆盖4个面部区域的17项面部特征，以及涵盖基频、响度调节、发声不稳与时域动态特性的42项言语特征。