Automatic Measurement of Postural Abnormalities With a Pose Estimation Algorithm in Parkinson's Disease

Abstract

Objective This study aims to develop an automated and objective tool to evaluate postural abnormalities in Parkinson's disease (PD) patients. Methods We applied a deep learning-based pose-estimation algorithm to lateral photos of prospectively enrolled PD patients (n = 28). We automatically measured the anterior flexion angle (AFA) and dropped head angle (DHA), which were validated with conventional manual labeling methods. Results The automatically measured DHA and AFA were in excellent agreement with manual labeling methods (intraclass correlation coefficient > 0.95) with mean bias equal to or less than 3 degrees. Conclusion The deep learning-based pose-estimation algorithm objectively measured postural abnormalities in PD patients.