Noh Won-Jun, Lee Mu Sook, Lee Byoung-Dai
Department of Computer Science, Graduate School, Kyonggi University, Suwon-si, Gyeonggi-do, 16227, Republic of Korea.
Department of Radiology, Keimyung University Dongsan Hospital, Daegu, 24601, Republic of Korea.
Sci Rep. 2024 Aug 8;14(1):18411. doi: 10.1038/s41598-024-69549-3.
This study aimed to develop and evaluate a deep learning-based system for the automatic measurement of angles (specifically, Meary's angle and calcaneal pitch) in weight-bearing lateral radiographs of the foot for flatfoot diagnosis. We utilized 3960 lateral radiographs, either from the left or right foot, sourced from a pool of 4000 patients to construct and evaluate a deep learning-based model. These radiographs were captured between June and November 2021, and patients who had undergone total ankle replacement surgery or ankle arthrodesis surgery were excluded. Various methods, including correlation analysis, Bland-Altman plots, and paired T-tests, were employed to assess the concordance between the angles automatically measured using the system and those assessed by clinical experts. The evaluation dataset comprised 150 weight-bearing radiographs from 150 patients. In all test cases, the angles automatically computed using the deep learning-based system were in good agreement with the reference standards (Meary's angle: Pearson correlation coefficient (PCC) = 0.964, intraclass correlation coefficient (ICC) = 0.963, concordance correlation coefficient (CCC) = 0.963, p-value = 0.632, mean absolute error (MAE) = 1.59°; calcaneal pitch: PCC = 0.988, ICC = 0.987, CCC = 0.987, p-value = 0.055, MAE = 0.63°). The average time required for angle measurement using only the CPU to execute the deep learning-based system was 11 ± 1 s. The deep learning-based automatic angle measurement system, a tool for diagnosing flatfoot, demonstrated comparable accuracy and reliability with the results obtained by medical professionals for patients without internal fixation devices.
本研究旨在开发并评估一种基于深度学习的系统，用于在负重足部侧位X线片上自动测量角度（具体为Meary角和跟骨倾斜角），以辅助扁平足诊断。我们利用从4000名患者中收集的3960张左右侧足部侧位X线片来构建和评估基于深度学习的模型。这些X线片拍摄于2021年6月至11月期间，排除了接受过全踝关节置换手术或踝关节融合手术的患者。采用了多种方法，包括相关性分析、Bland-Altman图和配对t检验，以评估使用该系统自动测量的角度与临床专家评估的角度之间的一致性。评估数据集包括来自150名患者的150张负重X线片。在所有测试案例中，使用基于深度学习的系统自动计算的角度与参考标准高度一致（Meary角：皮尔逊相关系数（PCC）=0.964，组内相关系数（ICC）=0.963，一致性相关系数（CCC）=0.963，p值=0.632，平均绝对误差（MAE）=1.59°；跟骨倾斜角：PCC=0.988，ICC=0.987，CCC=0.987，p值=0.055，MAE=0.63°）。仅使用CPU执行基于深度学习的系统进行角度测量所需的平均时间为11±1秒。基于深度学习的自动角度测量系统作为一种扁平足诊断工具，对于没有内固定装置的患者，其准确性和可靠性与医学专业人员获得的结果相当。
