Objective:To explore the feasibility of using deep learning methods in establishing MRI diagnostic models for cervical spondylosis.Methods:A retrospective collection of 514 MR images of patients were diagnosed with cervical spondylosis in our hospital from October 2020 to March 2023.Use existing cervical spine segmentation models to predict the subarachnoid space,spinal cord,inter-vertebral disc,posterior longitudinal ligament,and ligamentum flavum on axial T2WI,and predict the cervical vertebral bodies and intervertebral disc on sagittal T1 WI and T2 WI.A junior radiologist(with 2 years of experience)revised the labeling,and another senior radiologist(with≥15 years of experi-ence)reviewed the labels.According to the different radiologic sign of cervical spondylosis,3D or 2D U-net deep learning classification model training is carried out,including cervical vertebral hyperplasia model,cervical spondylolisthesis model,cervical disc herniation classification model,thickness of pos-terior longitudinal ligament model,and thickness of ligament flavum model.The output results of the models were imported into R programming software for confusion matrix analysis and ROC curve drawing.And the classification performance of the five models(accuracy,sensitivity,specificity,posi-tive predictive value,negative predictive value,and area under the ROC curve)was evaluated.Results:Among the five classification models,the cervical disc herniation classification model had the best di-agnostic performance,with a correct rate of 0.90,sensitivity of 0.95,specificity of 0.85,and area under the ROC curve of 0.982.The correct rate of cervical vertebral hyperplasia and spondylolisthesis also reached 0.81 and 0.80,the sensitivity was 0.74 and 0.76,the specificity was 0.84 and 1.00,and the area under the ROC curve was 0.855 and 0.905 respectively.The correct rates of the models for thickening of the posterior longitudinal ligament and ligamentum flavum were 0.82 and 0.77,respectively,with sensitivities of 0.78 and 0.84,specificities of 0.86 and 0.70,and areas under the ROC curve of 0.902 and 0.929,respectively.Conclusion:In the study,deep learning method based on MRI was used to establish the automatic classification diagnosis model of cervical spondylosis,and the classification model was trained for cervical vertebral body hyperplasia,slippage,intervertebral disc herniation,thickness of posterior longitudinal ligament and ligamentum flavum.The study proved that deep learning method based on MRI can be used in the computer aided diagnosis of cervical spondylosis,which laid the groundwork and foundation of the development of structured reports of cervical spine.
Magnetic resonance imagingCervical spondylosisDeep learning
NETL
NSTL
万方数据
