Abstract:Objective To compare the sensitivity, accuracy and false positive rate of arti?cial intelligence (AI) auxiliary diagnosis system in detecting pulmonary nodules in different slice thickness images of chest CT, and evaluate the optimal slice thickness suitable for AI detection. Methods A total of 190 chest CT cases were prospectively collected by Siemens dual source CT and reconstructed with 1/2/3 mm thick bone algorithm. The computer-aided diagnosis system based on deep learning was used to automatically detect nodules in three groups of images. The AI automatic detection results were compared with the gold standard, and the sensitivity and false positive rate of each group were calculated and compared. Results The number of lung nodules automatically detected by the 1 mm, 2 mm, 3 mm layer thickness image were 1403, 853, 1077, and the correct number of nodules were 1103, 607, 401, the sensitivity was 0.833±0.195, 0.473±0.258, 0.301±0.239, and the false positive rate was 1.58/CT, 1.29/CT, 3.56/CT, respectively. Conclusion The sensitivity of chest CT images with slice thickness of 2 mm in detecting subsolid nodules with size larger than 4 mm was not weaker than that of chest CT images with slice thickness of 1 mm, and the false positive rate was lower than that of chest CT images with slice thickness of 1 mm. Comprehensive evaluation, the overall detection ef?ciency of 1 mm layer thickness is better than other layer thicknesses.
Sui Y,Wei Y,Zhao D.Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE[J].Comput Math Methods Med,2015,12(8):368-373.