A Deep Learning Approach for MRI in the Diagnosis of Labral Injuries of the Hip Joint.

Abstract

The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses. To explore the feasibility of applying deep learning to diagnose and classify labral injuries with MRI. Retrospective. A total of 1016 patients were divided into normal (n=168, class 0) and abnormal labrum (n=848) groups. The abnormal group consisted of n=111 with class 1 (degeneration), n=437 with class 2 (partial or complete tear), and n=300 with unclassified injury. Patients were randomly divided into training, validation, and test cohort according to the ratio of 55%:15%:30%. Fat-saturation proton density-weighted fast spin-echo sequence at 3.0 T. Convolutional neural network-6 (CNN-6) was used to extract, discriminate, and detect oblique coronal (OCOR) and oblique sagittal (OSAG) images. Mask R-CNN was used for segmentation. LeNet-5 was used to diagnose and classify labral injuries. The weighting method combined the models of OCOR and OSAG. The output-input connection was used to correlate the whole diagnosis/classification system. Four radiologists performed subjective diagnoses to obtain the diagnosis results. CNN-6 and LeNet-5 were evaluated by area under the receiver operating characteristic (ROC) curve and related parameters. The mean average precision (MAP) evaluated the Mask R-CNN. McNemar's test was used to compare the radiologists and models. A P value < 0.05 was considered statistically significant. The area under the curve (AUC) of CNN-6 was 0.99 for extraction, discrimination, and detection. MAP values of Mask R-CNN for OCOR and OSAG image segmentation were 0.96 and 0.99. The accuracies of LeNet-5 in the diagnosis and classification were 0.94/0.94 (OCOR) and 0.92/0.91 (OSAG), respectively. The accuracy of the weighted models in the diagnosis and classification were 0.94 and 0.97, respectively. The accuracies of radiologists in the diagnosis and classification of labrum injuries ranged from 0.85 to 0.92 and 0.78 to 0.94, respectively. Deep learning can assist radiologists in diagnosing and classifying labrum injuries. 3 TECHNICAL EFFICACY: Stage 2.