Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

• Published in: Frontiers in neurology Vol. 12; p. 742654
• Main Authors: Monje, Mariana H. G., Domínguez, Sergio, Vera-Olmos, Javier, Antonini, Angelo, Mestre, Tiago A., Malpica, Norberto, Sánchez-Ferro, Álvaro
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 23.12.2021
• Subjects: artificial intelligence and bio-inspired algorithms; kinematics; Neurology; Parkinson's disease; telemedicine; webcam; telemedicine; kinematics; Parkinson's disease; webcam; artificial intelligence and bio-inspired algorithms
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2021.742654

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.