  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.
