A prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool (www.parktest.net). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.

帕金森病 (PD) 的一个普遍症状是缺乏表情——面部表情减少。在本文中，我们提出了一种利用微表情研究来诊断帕金森病的方法。我们分析了 604 个人（61 名患有 PD 和 543 名无 PD，平均年龄 63.9 岁，标准差为 7.8 岁）的 1812 个视频中的面部动作单位（AU），这些视频是通过网络工具（www.parktest）在线收集的。网）。在这些视频中，参与者被要求做出三种面部表情（微笑、厌恶和惊讶的表情），然后做出中性表情。利用计算机视觉和机器学习技术，我们客观地测量了面部肌肉运动的方差，并用它来区分患有和不患有帕金森病的个体。使用面部微表情的预测准确性与利用运动症状的方法相当。 Logistic 回归分析显示，与非 PD 个体相比，PD 参与者在 AU6（抬腮）、AU12（拉唇角）和 AU4（降眉）方面的方差较小。使用支持向量机的自动分类器接受了方差训练，达到了 95.6% 的准确率。使用面部表情作为未来 PD 的数字生物标志物，对于因身体分离（例如，由于新冠肺炎）或不动而需要远程诊断的患者来说可能具有潜在的变革性。