https://www.selleckchem.com/products/kya1797k.html
This study aims to develop an automated and objective tool to evaluate postural abnormalities in Parkinson's disease (PD) patients. 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. The automatically measured DHA and AFA were in excellent agreement with manual labeling methods (intraclass correlation coefficient
Like
Comment
Share
