Abstract
Parkinson's disease (PD) diagnosis remains challenging due to lacking a reliable biomarker and limited access to clinical care. In this study, we present an analysis of the largest video dataset containing micro-expressions to screen for PD. We collected 3,871 videos from 1,059 unique participants, including 256 self-reported PD patients. The recordings are from diverse sources encompassing participants' homes across multiple countries, a clinic, and a PD care facility in the US. Leveraging facial landmarks and action units, we extracted features relevant to Hypomimia, a prominent symptom of PD characterized by reduced facial expressions. An ensemble of AI models trained on these features achieved an accuracy of 89.7% and an Area Under the Receiver Operating Characteristic (AUROC) of 89.3% while being free from detectable bias across population subgroups based on sex and ethnicity on held-out data. Further analysis reveals that features from the smiling videos alone lead to comparable performance, even on two external test sets the model has never seen during training, suggesting the potential for PD risk assessment from smiling selfie videos.
Abstract (translated)
帕金森病(PD)的诊断仍然面临挑战,由于缺乏可靠的生物标记和临床护理的有限访问,因此难以进行准确诊断。在本研究中,我们分析了包含微表情的视频数据集,以检测PD。我们从1,059个独特的参与者中收集了3,871个视频,其中包括256名自我报告的PD患者。这些记录来自多个国家、一家诊所和在美国的一家PD护理设施。利用面部地标和行动单元,我们提取了与低表情减少相关的特征。基于这些特征训练的AI模型在准确性和AUROC方面取得了89.7%和89.3%的水平,而保留的数据集排除了基于性别和种族的可检测偏见。进一步分析表明,仅从微笑着的视频中提取的特征会导致相似的性能,即使在训练期间模型从未见过的两个外部测试集上也是如此,这表明从微笑自拍照视频中进行PD风险评估的潜力。
URL
https://arxiv.org/abs/2308.02588
