Detection Efficiency of Residents Assisted by Artificial Intelligence for Pulmonary Solid Nodules with Different Sizes: A Preliminary Study

Objective:To investigate the clinical application value of artificial intelligence(AI) assisted detection software for improving the detection efficiency of pulmonary solid nodules in inexperienced residents. Methods:A total of 200 CT images of pulmonary solid nodules confirmed by CT were collected. One senior radiologist with more than 8 years' experience read CT images based on the initial diagnosis of another senior radiologist with similar experience and a final decision was subsequently conducted by deputy chief radiologist with more than 15 years' experience to determine the ground truth solid lung nodules. One resident read the images without AI software(method A) and the same resident read CT images with AI software(method B) after two weeks' washout period(method B). The results of methods A and B were compared with the gold standard nodules. The number of true positive nodules and the number of false positive nodules were recorded. The difference between detection sensitivity and false positive rate between the two groups was analyzed by SPSS 20.0. The difference was statistically significant(P < 0.05). Results:Compared with method A, the sensitivity of method B increased significantly, the sensitivity of total solid nodules increased by 65%, and the false positive rate decreased by 25%, the difference was statistically significant(P < 0.05); The sensitivity of the four groups of different sizes(D ≤ 4 mm, 4 mm < D ≤ 6 mm, 6 mm < D ≤ 8 mm, D > 8 mm) for lung solid nodules was improved by AI assisted software, and the increase rate was 78%,38%, 27% and 13.8%, respectively. The area on the FROC curve of method A(AAC) was 0.176, the method B was 0.0852, and the difference was statistically significant(P < 0.05). The average reading time of the two methods A and B was 411.9 seconds and 319.7 seconds respectively. Conclusion:AI assisted software can significantly improve the detection efficiency of inexperienced residents for different sizes of lung solid nodules in CT, especially for the detection of lung solid nodules ≤ 4 mm in diameter.