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

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.