基于深度学习的继发性肺结核CT辅助诊断模型构建及验证OA北大核心CSTPCD

Objective:To develop a deep learning-based auxiliary diagnostic model for secondary tuberculosis using CT scans and evaluate its clinical applicability.Methods:A retrospective collection was conducted on clinical data of 2004 patients who underwent chest CT scans at the Chongqing Public Health Medical Center from December 2018 to April 2023.The patients were divided into three groups:secondary tuberculosis(934 patients),ordinary lung infection(526 patients),and normal lungs(544 patients).Using a completely random sampling method,the dataset was divided into a training set(1402 patients,70.0％)and a test set(602 patients,30.0％).An automatic lung field segmentation algorithm was applied to isolate the lung field in all images.BasicNet and DenseNet classification algorithms were used for categorize the three groups.The discriminative performance of the model was evaluated using metrics such as area under curve(AUC),sensitivity,specificity,and accuracy.Finally,the optimal model was compared with three radiologists of different years of experience using testing data.Results;Using 602 samples in an independent test set,the DenseNet model demonstrated superior performance compared to the BasicNet model.They achieved an average AUC,sensitivity,specificity,and accuracy of 92.1％vs.89.4％,79.7％vs.74.0％,89.4％vs.86.6％,and 86.2％vs.83.3％,respectively.The diagnostic performance of the DenseNet model was superior to that of young doctors(accuracy:90.7％and 89.1％,Kappa=0.677)and exhibited high diagnostic consistency with middle and highly experienced radiologists without any significant difference(accuracy:90.7％,92.2％and 95.3％,Kappa=0.746,0.819).Conclusion:The DenseNet model can accurately identify secondary tuberculosis,achieving a competency level similar to a middle experienced radiologist,making it a potential auxiliary diagnostic tool for secondary tuberculosis.