Bengbu Medical College, Bengbu 233000, China; Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou 310014, China.
Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou 310014, China.
Eur J Radiol. 2020 Nov;132:109303. doi: 10.1016/j.ejrad.2020.109303. Epub 2020 Sep 25.
To develop and evaluate an automatic measurement model for hip joints based on anteroposterior (AP) pelvic radiography and a deep learning algorithm.
A total of 1260 AP pelvic radiographs were included. 1060 radiographs were randomly sampled for training and validation and 200 radiographs were used as the test set. Landmarks for four commonly used parameters, such as the center-edge (CE) angle of Wiberg, Tönnis angle, sharp angle, and femoral head extrusion index (FHEI), were identified and labeled. An encoder-decoder convolutional neural network was developed to output a multi-channel heat map. Measurements were obtained through landmarks on the test set. Right and left hips were analyzed respectively. The mean of each parameter obtained by three radiologists was used as the reference standard. The Percentage of Correct Key points (PCK), intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Bland-Altman plots were used to determine the performance of deep learning algorithm.
PCK of the model at 3 mm distance threshold range was from 87 % to 100 %. The CE angle, Tönnis angle, Sharp angle and FHEI of the left hip generated by the model were 29.8°±6.1°, 5.6°±4.2°, 39.0°±3.5° and 19 %±5 %, respectively. The parameters of the right hip were 30.4°±6.1°, 7.1°±4.4°, 38.9°±3.7° and 18 %±5 %. There were good correlation and consistency of the four parameters between the model and the reference standard (ICC 0.83-0.93, r 0.83-0.93, RMSE 0.02-3.27, MAE 0.02-1.79).
The new developed model based on deep learning algorithm can accurately identify landmarks on AP pelvic radiography and automatically generate parameters of hip joint. It will provide convenience for clinical practice of measurement.
开发并评估一种基于骨盆前后位（AP）X 线片和深度学习算法的髋关节自动测量模型。
共纳入 1260 张 AP 骨盆 X 线片。其中 1060 张随机抽样用于训练和验证，200 张用于测试集。对常用的 4 个参数（Wiberg 的中心边缘角、Tönnis 角、锐度角和股骨头外突指数）的标志点进行识别和标记。开发了一个编码器-解码器卷积神经网络，以输出多通道热图。通过测试集上的标志点获取测量值。分别分析左右髋关节。三位放射科医生测量的每个参数的平均值作为参考标准。使用关键点正确百分比（PCK）、组内相关系数（ICC）、Pearson 相关系数（r）、均方根误差（RMSE）、平均绝对误差（MAE）和 Bland-Altman 图来确定深度学习算法的性能。
在 3mm 距离阈值范围内，模型的 PCK 为 87%至 100%。模型生成的左侧髋关节 CE 角、Tönnis 角、Sharp 角和 FHEI 分别为 29.8°±6.1°、5.6°±4.2°、39.0°±3.5°和 19%±5%。右侧髋关节的参数分别为 30.4°±6.1°、7.1°±4.4°、38.9°±3.7%和 18%±5%。模型与参考标准之间的四个参数相关性和一致性较好（ICC 0.83-0.93，r 0.83-0.93，RMSE 0.02-3.27，MAE 0.02-1.79）。
基于深度学习算法的新模型可以准确识别 AP 骨盆 X 线片上的标志点，并自动生成髋关节参数。这将为髋关节测量的临床实践提供便利。
