Convolutional Neural Network Algorithm Trained with Anteroposterior Radiographs to Diagnose Pre-Collapse Osteonecrosis of the Femoral Head

Abstract

A convolutional neural network (CNN) is a representative deep-learning algorithm that has a significant advantage in image recognition and classification. Using anteroposterior pelvic radiographs as input data, we developed a CNN algorithm to determine the presence of pre-collapse osteonecrosis of the femoral head (ONFH). We developed a CNN algorithm to differentiate between ONFH and normal radiographs. We retrospectively included 305 anteroposterior pelvic radiographs (right hip: pre-collapsed ONFH = 79, normal = 226; left hip: pre-collapsed ONFH = 62, normal = 243) as data samples. Pre-collapsed ONFH was diagnosed using pelvic magnetic resonance imaging data for each patient. Among the 305 cases, 69.8% of the included data samples were randomly selected as the training set, 21.0% were selected as the validation set, and the remaining 9.2% were selected as the test set to evaluate the performance of the developed CNN algorithm. The area under the curve of our developed CNN algorithm on the test data was 0.912 (95% confidence interval, 0.773–1.000) for the right hip and 0.902 (95% confidence interval, 0.747–1.000) for the left hip. We showed that a CNN algorithm trained using pelvic radiographs would help diagnose pre-collapse ONFH.