基于半监督网络的前交叉韧带损伤膝关节磁共振诊断辅助研究OACSTPCD

Objective To explore the application of semi-supervised algorithm residual network(SMRNet)deep learning in computer-aided diagnosis of anterior cruciate ligament(ACL)injury in magnetic resonance images(MRI).Methods One hundred patients with arthroscopically confirmed ACL injury,and 100 patients who underwent arthroscopic examination and had no ACL injury(control group)were enrolled in the study.Preoperative MRI images of all subjects were analyzed,the appropriate layers were selected and cropped for SMRNet training.The ACL injury on a single MRI slice was determined by SMRNet and 4 senior clinicians(2 radiologists and 2 orthopedists).With the intraoperative finding as gold standard,the performance in diagnosis of ACL injury on MRI images was evaluated for two groups.Results The sensitivity,specificity,accuracy,positive predictive value and negative predictive value of SMRNet classification were 97.00% ,94.00% ,95.50% ,94.17% and 96.91% ,respectively;while the results from clinicians were similar,with sensitivity range of 91.00% -96.00% ,specificity range of 90.00% -94.00% ,accuracy range of 90.50% -95.00% ,positive predictive value range of 90.09% -94.12% ,negative predictive value range of 90.90% -95.92% ,and there were no significant differences between them(P＞0.05).Conclusion The diagnostic results of ACL on MRI images given by trained SMRNet model are similar to those given by senior clinicians,indicates that computer-aided diagnosis based on SMRNet deep learning is expected to become an important tool for clinical application in the future.