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

作者：Ming Ni, Xiaoyi Wen, Wen Chen, Yuqing Zhao, Yuan Yuan, Piaoe Zeng, Qizheng Wang, Yong Wang, Huishu Yuan · 发表于：Journal of Magnetic Resonance Imaging · 年份：2022 · DOI：10.1002/jmri.28069 · 被引用次数：17 · 研究领域：Hip disorders and treatments、Sports injuries and prevention、Bone and Joint Diseases

BACKGROUND: The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses. PURPOSE: To explore the feasibility of applying deep learning to diagnose and classify labral injuries with MRI. STUDY TYPE: Retrospective. POPULATION: 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%. FIELD STRENGTH/SEQUENCE: Fat-saturation proton density-weighted fast spin-echo sequence at 3.0 T. ASSESSMENT: 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. STATISTICAL TESTS: 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 ...