Screening for Parkinson’s disease using “computer vision”

Abstract

Background Identifying bradykinesia is crucial for diagnosing Parkinson’s disease (PD). Traditionally, the finger-tapping test has been used, relying on subjective assessments by physicians. Computer vision offers a non-contact and cost-effective alternative for assessing Parkinson’s disease. Objective This study aimed to detect Parkinson’s disease by identifying bradykinesia using computer vision in the finger-tapping test and applying machine learning techniques for both hands. Methods We recruited 100 patients with PD and healthy controls. Four neurologists assessed bradykinesia, and 10-second smartphone-recorded finger-tapping movements were analyzed using Google MediaPipe Hands software. Six machine learning models were trained using a nested cross-validation framework. Results The differences in tapping scores between the left and right hands were significantly greater in the PD group (2.8 (5.0) vs 0.4 (0.7), p = 0.001) than in the healthy controls. Moreover, the tapping amplitude variation and all amplitude decremental parameters in the PD group differed significantly from those of the standard controls. The PD group had significantly lower tapping scores than the normal subjects (right: 17.9 (7.8)/ left: 17.9 (5.6) vs. right: 24.6 (7.3)/ left: 24.6 (7.2), p < 0.001). The support vector machine outperformed the other models. The most influential features were the tapping difference, followed by the tapping score (right hand) and tapping amplitude mean (right hand). Conclusions A computer vision method can accurately detect bradykinesia using the tapping feature from the finger-tapping method, which involves the simultaneous tapping of both hands.