PDF(Pubmed)
Abstract：
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.
摘要：
本研究旨在开发和评估基于深度学习的自动角度测量系统(具体来说，足部负重侧位X线片中的米里角和跟骨间距），用于诊断扁平足。我们用了3960张横向射线照片,无论是左脚还是右脚，来自4000名患者，以构建和评估基于深度学习的模型。这些X射线照片是在2021年6月至11月之间拍摄的，并且排除了接受全踝关节置换手术或踝关节固定术的患者。各种方法,包括相关分析，Bland-Altman阴谋,配对T检验，用于评估使用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张负重射线照片。在所有测试用例中，使用基于深度学习的系统自动计算的角度与参考标准非常吻合(Meary's角度: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°;ccalcanealpitch:PCC=88,ICC=0.987,MACCC值=0.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±1s。基于深度学习的自动角度测量系统，诊断扁平足的工具，对于没有内固定装置的患者，与医疗专业人员获得的结果具有可比性和可靠性。
