Parkinsonism & related disorders · 2026
Development of a dynamics-enhanced facial algorithm to discriminate progressive supranuclear palsy (PSP) from Parkinson's disease (PD).
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Ruan Zheng, Liu Jingyue, Guo Weihang, Zhang Yanyan, Yu Yan, Chhetri Jagadish Kumar, Chan Piu
Аннотация
BACKGROUND: Progressive supranuclear palsy (PSP) and Parkinson's disease (PD) share overlapping motor features, making early differential diagnosis challenging in clinical practice. Facial hypomimia is common in both conditions, but disease-specific differences in facial dynamics may provide discriminative diagnostic cues.
METHODS: We recruited 62 participants (27 PSP and 35 PD) from a tertiary movement-disorders center. Facial videos were recorded during a standardized expression paradigm including smiling, anger, surprise, and rapid blinking. Dynamic geometric and velocity-based facial features were extracted using a 468-point MediaPipe facial landmark framework and normalized to physiologically meaningful reference distances. Machine-learning models-including support vector machine (SVM), random forest (RF), and gradient boosting (GB)-were trained using nested cross-validation. An independent 20% hold-out set was reserved for external-style validation. Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Feature importance was assessed using F-scores, mutual information, RF importance, and SHAP analysis.
RESULTS: In cross-validation, the RF model achieved accuracy of 0.902, compared with 0.875 for SVM and 0.888 for the GB model. In the independent hold-out set, the RF classifier achieved an accuracy of 0.82 and an AUC of 0.77 (95% CI 0.63-0.89). Key features associated with group differences included higher mouth-opening velocity during surprise, reduced brow-eye distances, and greater forehead-wrinkle variability in PSP compared with PD.
CONCLUSIONS: Quantitative facial dynamics may provide interpretable digital biomarkers that can aid in differentiating PSP from PD. This facial-dynamics-based approach shows promise as a non-invasive adjunct for differential diagnosis, particularly in patients with overlapping early clinical presentations.
METHODS: We recruited 62 participants (27 PSP and 35 PD) from a tertiary movement-disorders center. Facial videos were recorded during a standardized expression paradigm including smiling, anger, surprise, and rapid blinking. Dynamic geometric and velocity-based facial features were extracted using a 468-point MediaPipe facial landmark framework and normalized to physiologically meaningful reference distances. Machine-learning models-including support vector machine (SVM), random forest (RF), and gradient boosting (GB)-were trained using nested cross-validation. An independent 20% hold-out set was reserved for external-style validation. Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Feature importance was assessed using F-scores, mutual information, RF importance, and SHAP analysis.
RESULTS: In cross-validation, the RF model achieved accuracy of 0.902, compared with 0.875 for SVM and 0.888 for the GB model. In the independent hold-out set, the RF classifier achieved an accuracy of 0.82 and an AUC of 0.77 (95% CI 0.63-0.89). Key features associated with group differences included higher mouth-opening velocity during surprise, reduced brow-eye distances, and greater forehead-wrinkle variability in PSP compared with PD.
CONCLUSIONS: Quantitative facial dynamics may provide interpretable digital biomarkers that can aid in differentiating PSP from PD. This facial-dynamics-based approach shows promise as a non-invasive adjunct for differential diagnosis, particularly in patients with overlapping early clinical presentations.