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.
Keywords: Camptocormia; Parkinson’s disease; Pose estimation.