Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence - Monje, Mariana H. G.; Domínguez, Sergio; Vera-Olmos, Javier; Antonini, Angelo; Mestre, Tiago A.; Malpica, Norberto; Sánchez-Ferro, Álvaro (Frontiers in Neurology, 2021)
ABSTRACT Objective: To proof the concept of a new optical video-based system to measure Parkinson`s disease remotely using an accessible standard webcam. Methods: We consecutively enrolled a cohort of 42 Parkinson’s Disease patients and healthy subjects. Participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. Video frames were processed using artificial intelligence algorithms tracking the hands´ movements. 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. The developed classifiers were validated on an independent dataset. Results: We found significant differences in the motor performance of Parkinson’s disease patients and healthy subjects in all bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with inertial measurement units for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with existing methods. Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect 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 Parkinson’s disease management.