AI-Enabled Parkinson’s Disease Screening Using Smile Videos

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 Parkinson’s Disease May Have Link to Stroke March 2017  

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AI-Enabled Parkinson’s Disease Screening Using Smile Videos

Authors: Adnan M.Sc. https://orcid.org/0000-0002-8012-6679 Islam M.Sc. https://orcid.org/0000-0003-3725-3493 Sangwu B.Sc. https://orcid.org/0000-0003-3930-3079 E.M. Ph.D. https://orcid.org/0000-0002-9736-442X Sutapa Dey Tithi B.Sc. https://orcid.org/0009-0007-5815-882X Kazi Noshin B.Sc. https://orcid.org/0009-0008-6015-8236 Md Rayhanul Islam M.Sc. https://orcid.org/0000-0001-8384-6180 Hoque Ph.D. https://orcid.org/0000-0003-4781-4733 Author Info & Affiliations Published  NEJM AI 2 NO.  7 Copyright © 2025 Information & Authors

Abstract

Background Parkinson’s disease (PD) diagnosis is challenging owing to insufficient access to clinical care. We present an efficient and accessible artificial intelligence–driven PD screening method leveraging the largest video dataset of facial expressions from 1452 unique participants, including 391 with PD — 300 of whom have been clinically diagnosed and 91 of whom self-reported the condition.

We recruited individuals across multiple countries — primarily North America — via social media, email outreach, and a PD research registry; patients undergoing in-person PD assessments at a U.S. clinic; clients of a U.S.-based PD wellness center and their caregivers; and individuals in Bangladesh identified as being at high risk for PD. Participants used an online tool to record themselves (either at home or in a clinical setting) mimicking three facial expressions (i.e., smile, disgust, and surprise). Facial landmarks and action unit–based features were extracted to quantify hypomimia. Machine-learning models were trained on these features to distinguish between individuals with and without PD. The model’s generalizability was tested on external test datasets (from the U.S. clinic and Bangladesh).ResultsAn ensemble of models trained on smile videos achieved an accuracy of 87.9 ± 0.1% and an area under the receiver operating characteristic curve (AUROC) of 89.3 ± 0.3% in 10-fold cross-validation, with a 76.8 ± 0.4% sensitivity, 91.4 ± 0.3% specificity, 73.3 ± 0.5% positive predictive value (PPV), and 92.7 ± 0.1% negative predictive value (NPV). On the U.S. clinic test set, it achieved an 80.3 ± 1.6% accuracy and an 83.3 ± 1.4% AUROC, with a 80.0 ± 2.5% sensitivity, and 80.5 ± 2.0% specificity. On the test set from Bangladesh, performance reached an 85.3 ± 1.4% accuracy with an 81.5 ± 1.8% AUROC. The specificity, sensitivity, and NPV remained competitive, while PPV declined to 35.7 ± 4.8%. No detectable bias was observed across sex and ethnic subgroups, except on the test dataset from Bangladesh, for which performance was significantly better for female participants.Conclusions Smiling videos can effectively differentiate between individuals with and without PD, offering a potentially easy, accessible, and cost-efficient way to screen for PD, especially when access to clinical diagnosis is limited. (Funded by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health number, P50NS108676 and others.)