Feasibility of Automatic Measurements of Hip Joints Based on Pelvic Radiography and a Deep Learning Algorithm

Abstract Purpose To develop and evaluate an automatic measurement model for hip joints based on anteroposterior (AP) pelvic radiography and a deep learning algorithm. Methods 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, Tonnis 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. Results PCK of the model at 3 mm distance threshold range was from 87% to 100%. The CE angle, Tonnis 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 to 0.93, r 0.83 to 0.93, RMSE 0.02 to 3.27, MAE 0.02 to 1.79). Conclusions 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.