Noh Seungha, Lee Mu Sook, Lee Byoung-Dai
Department of Computer Science, Graduate School, Kyonggi University, Suwon-si, Republic of Korea.
Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea.
Sci Rep. 2025 Apr 29;15(1):15012. doi: 10.1038/s41598-025-99620-6.
This study developed and evaluated a deep learning (DL)-based system for automatically measuring talar tilt and anterior talar translation on weight-bearing ankle radiographs, which are key parameters in diagnosing ankle joint instability. The system was trained and tested using a dataset comprising of 1,452 anteroposterior radiographs (mean age ± standard deviation [SD]: 43.70 ± 22.60 years; age range: 6-87 years; males: 733, females: 719) and 2,984 lateral radiographs (mean age ± SD: 44.37 ± 22.72 years; age range: 6-92 years; male: 1,533, female: 1,451) from a total of 4,000 patients, provided by the National Information Society Agency. Patients who underwent joint fusion, bone grafting, or joint replacement were excluded. Statistical analyses, including correlation coefficient analysis and Bland-Altman plots, were conducted to assess the agreement and consistency between the DL-calculated and clinician-assessed measurements. The system demonstrated high accuracy, with strong correlations for talar tilt (Pearson correlation coefficient [r] = 0.798 (p < .001); intraclass correlation coefficient [ICC] = 0.797 [95% CI 0.74, 0.82]; concordance correlation coefficient [CCC] = 0.796 [95% CI 0.69, 0.85]; mean absolute error [MAE] = 1.088° [95% CI 0.06°, 1.14°]; mean square error [MSE] = 1.780° [95% CI 1.69°, 2.73°]; root mean square error [RMSE] = 1.374° [95% CI 1.31°, 1.44°]; 95% limit of agreement [LoA], 2.0° to - 2.3°) and anterior talar translation (r = .862 (p < .001); ICC = 0.861 [95% CI 0.84, 0.89]; CCC = 0.861 [95% CI 0.86, 0.89]; MAE = 0.468 mm [95% CI 0.42 mm, 0.51 mm]; MSE = 0.551 mm [95% CI 0.49 mm, 0.61 mm]; RMSE = 0.742 mm [95% CI 0.69 mm, 0.79 mm]; 95% LoA, 1.5 mm to - 1.3 mm). These results demonstrate the system's capability to provide objective and reproducible measurements, supporting clinical interpretation of ankle instability in routine radiographic practice.
本研究开发并评估了一种基于深度学习（DL）的系统，用于在负重踝关节X线片上自动测量距骨倾斜和距骨前移，这是诊断踝关节不稳的关键参数。该系统使用由国家信息社会局提供的、来自4000名患者的数据集进行训练和测试，该数据集包括1452张前后位X线片（平均年龄±标准差[SD]：43.70±22.60岁；年龄范围：6 - 87岁；男性：733例，女性：719例）和2984张侧位X线片（平均年龄±SD：44.37±22.72岁；年龄范围：6 - 92岁；男性：1533例，女性：1451例）。接受关节融合、骨移植或关节置换的患者被排除。进行了包括相关系数分析和Bland - Altman图在内的统计分析，以评估深度学习计算的测量值与临床医生评估的测量值之间的一致性和可靠性。该系统显示出高准确性，距骨倾斜的相关性很强（Pearson相关系数[r]=0.798（p<0.001）；组内相关系数[ICC]=0.797[95%CI 0.74, 0.82]；一致性相关系数[CCC]=0.796[95%CI 0.69, 0.85]；平均绝对误差[MAE]=1.088°[95%CI 0.06°, 1.14°]；均方误差[MSE]=1.780°[95%CI 1.69°, 2.73°]；均方根误差[RMSE]=1.374°[95%CI 1.31°, 1.44°]；95%一致性界限[LoA]，2.0°至 - 2.3°）以及距骨前移（r = 0.862（p<0.001）；ICC = 0.861[95%CI 0.84, 0.89]；CCC = 0.861[95%CI 0.86, 0.89]；MAE = 0.468毫米[95%CI 0.42毫米, 0.51毫米]；MSE = 0.551毫米[95%CI 0.49毫米, 0.61毫米]；RMSE = 0.742毫米[95%CI 0.69毫米, 0.79毫米]；95%LoA，1.5毫米至 - 1.3毫米）。这些结果证明了该系统能够提供客观且可重复的测量值，支持在常规放射学实践中对踝关节不稳进行临床解读。
