Motor symptoms of Parkinson’s disease: critical markers for early AI-assisted diagnosis
PMC12313583
· 10.3389/fnagi.2025.1602426
Gap Declaration
This article provides an overview of AI-driven early detection approaches based on various motor symptoms of PD, including eye movement, facial expression, speech, handwriting, finger tapping, and gait. Specifically, we summarized the characteristic manifestations of these motor symptoms, analyzed the features of the data currently collected for AI-assisted diagnosis, collected the publicly available datasets, evaluated the performance of existing diagnostic models, and discussed their limitations. By scrutinizing the existing research methodologies, this review summarizes the application progress of motor symptom-based AI technology in the early detection of PD, explores the key challenges from experimental techniques to clinical translation applications, and proposes future research directions to promote the clinical practice of AI technology in PD diagnosis.
Abstract
Parkinson’s disease is a prevalent neurodegenerative disorder, where early diagnosis is essential for slowing disease progression and optimizing treatment strategies. The latest developments in artificial intelligence (AI) have introduced new opportunities for early detection. Studies have demonstrated that before obvious motor symptoms appear, PD patients exhibit a range of subtle but quantifiable motor abnormalities. This article provides an overview of AI-driven early detection approaches based on various motor symptoms of PD, including eye movement, facial expression, speech, handwriting, finger tapping, and gait. Specifically, we summarized the characteristic manifestations of these motor symptoms, analyzed the features of the data currently collected for AI-assisted diagnosis, collec…
Conclusions / Discussion
10 Conclusion This article reviews the role of the motor symptoms and corresponding characteristic manifestations of PD in AI-assisted diagnosis. At present, PD recognition technology based on speech, handwriting, Tap, and gait is relatively mature, but the extraction of dynamic features such as facial expressions and eye movements still needs to be strengthened. Establishing a standardized data collection process and a large-scale dataset containing multiple information labels is currently an urgent problem that needs to be solved. In the future, research on multimodal recognition models and wearable devices is a promising direction. These suggestions may provide better services for both patients and doctors and contribute to social healthcare.
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