Diagnostic Value of Artificial Intelligence Based on CT Image in Benign and Malignant Pulmonary Nodules

OBJECTIVE: To evaluate the diagnostic value of artificial intelligence-assisted CT imaging in benign and malignant pulmonary nodules. METHODS: The CT scan screening of pulmonary nodules from November 2018 to November 2020 was retrospectively collected. The diagnosis of pulmonary nodules and surgical treatment were performed. A total of 194 nodules in 152 patients with clear pathological results were observed. All patients underwent CT examination to analyze the consistency of the results of artificial intelligence, physician reading according to imaging features, multidisciplinary team work (MDT) diagnosis, and postoperative pathological results; the diagnostic efficacy of different diagnostic methods for solitary pulmonary nodules and the differences of ROC curve and AUC were analyzed. The accuracy, specificity, sensitivity, positive predictive value, negative predictive value, false negative rate, and false positive rate of different diagnostic methods for pulmonary nodules were calculated, and the ROC curves of different diagnostic methods were plotted. RESULTS: The accuracy, sensitivity, specificity, and Youden index of artificial intelligence (AI) were 89.69%, 92.98%, 65.22%, and 58.20%; the accuracy, sensitivity, specificity, and Youden index of physician reading were 85.57%, 88.30%, 65.22%, and 53.52%; the accuracy, sensitivity, specificity, and Youden index of MDT were 96.91%, 98.25%, 86.96%, and 85.21%, respectively. The kappa values of artificial intelligence, physician reading, and MDT were 0.541, 0.437, and 0.852, and the AUC was 0.768, 0.791, and 0.926, respectively (P < 0.001). The average detection time of pulmonary nodules in the AI group, manual reading group, and MAT group was (145 ± 97) s, (534 ± 297) s, and (421 ± 128) s (P < 0.001). CONCLUSION: Artificial intelligence pulmonary nodule detection system can improve the coincidence rate and accuracy of early diagnosis of lung cancer, shorten the average detection time, and provide more accurate information for clinical decision-making.