Ryu Seung Min, Shin Keewon, Doh Chang Hyun, Ben Hui, Park Ji Yeon, Koh Kyoung-Hwan, Shin Hangsik, Jeon In-Ho
Department of Orthopedic Surgery, Seoul Medical Center, Seoul, 02053, South Korea.
Department of Artificial Intelligence Research Center, Korea University College of Medicine, Anam Hospital, Seoul, 02841, South Korea.
Biomed Eng Lett. 2024 Sep 28;15(1):131-142. doi: 10.1007/s13534-024-00432-w. eCollection 2025 Jan.
Accurate assessment of shoulder range of motion (ROM) is crucial for evaluating patient progress. Traditional manual goniometry often lacks precision and is subject to inter-observer variability, especially in measuring shoulder internal rotation (IR). This study introduces an artificial intelligence (AI)-based approach that uses clinical photography to improve the accuracy of ROM quantification. We analyzed a total of 150 clinical photographs, including 100 shoulder and 50 elbow images, taken between January and April 2022. An MMPose model with an HR-NET backbone architecture, pre-trained on the COCO-WholeBody dataset, was used to detect 17 anatomical landmarks. A random forest classifier (PoseRF) then categorized poses, and ROM angles were calculated. Concurrently, two clinicians independently measured shoulder IR at the vertebral level, and inter-observer agreement was evaluated. Linear regression analyses were conducted to correlate the AI-derived measurements with the clinicians' assessments. The AI-based algorithm accurately detected anatomical landmarks in 96% of shoulder and 100% of elbow images. Pose detection achieved 95% accuracy overall, with 100% accuracy for specific shoulder (abduction, flexion, external rotation) and elbow (flexion, extension) poses. Intraclass correlation coefficients (ICCs) between the AI algorithm and human observers ranged from 0.965 to 0.997, indicating excellent inter-observer reliability. Kruskal-Wallis test showed no statistically significant differences in ROM measurements among the AI algorithm and two human observers across all joint angles ( > 0.05). The AI-based algorithm demonstrated performance comparable to that of human observers in quantifying shoulder and elbow ROM from clinical photographs. For shoulder internal rotation, the AI approach showed potential for improved consistency compared to traditional methods.
The online version contains supplementary material available at 10.1007/s13534-024-00432-w.
准确评估肩部活动范围（ROM）对于评估患者进展至关重要。传统的手动测角法往往缺乏精确性，并且存在观察者间的差异，尤其是在测量肩部内旋（IR）时。本研究引入了一种基于人工智能（AI）的方法，该方法使用临床摄影来提高ROM量化的准确性。我们分析了2022年1月至4月期间拍摄的总共150张临床照片，包括100张肩部和50张肘部图像。使用在COCO-WholeBody数据集上预训练的具有HR-NET骨干架构的MMPose模型来检测17个解剖标志点。然后，随机森林分类器（PoseRF）对姿势进行分类，并计算ROM角度。同时，两名临床医生独立测量椎体水平的肩部IR，并评估观察者间的一致性。进行线性回归分析以关联AI得出的测量结果与临床医生的评估。基于AI的算法在96%的肩部图像和100%的肘部图像中准确检测到解剖标志点。姿势检测总体准确率达到95%，对于特定的肩部（外展、屈曲、外旋）和肘部（屈曲、伸展）姿势准确率为100%。AI算法与人类观察者之间的组内相关系数（ICC）范围为0.965至0.997，表明观察者间可靠性极佳。Kruskal-Wallis检验显示，在所有关节角度上，AI算法与两名人类观察者之间的ROM测量值无统计学显著差异（>0.05）。基于AI的算法在从临床照片量化肩部和肘部ROM方面表现出与人类观察者相当的性能。对于肩部内旋，与传统方法相比，AI方法显示出提高一致性的潜力。
在线版本包含可在10.1007/s13534-024-00432-w获取的补充材料。
