Evaluation of the Diagnostic Value of Pulmonary Nodules Based on Two AI Software

Objective: To explore the clinical value of two kinds of AI detection software in ≥5 mm pulmonary nodules. Methods: A total of 92 patients with pulmonary nodules (483 nodules) were selected from the affiliated Hospital of Yan'an University between June and October 2021. The nodules detected by AI software were evaluated and the number and type of nodules were recorded by two senior radiologists. Two senior imaging doctors then evaluated the manual film reading, which was used as the gold standard for nodule recognition. Subsequently, the detection rate and false positive and negative rates of the two software were calculated, and the nodule detection value of the two AI software was evaluated. Additionally, the chi-square and Fisher precision tests were used to compare the differences between the different software and the gold standard. Finally, the diagnostic value of the combination of the two kinds of AI software for pulmonary nodules was calculated. Results: The detection rates of software A and software B nodules were 92.1% and 87.0%, respectively. Moreover, the coincidence degree between software A and manual reading was general (Kappa=0.213), while that between software B and manual reading was weak (Kappa=0.150). There was also a significant difference in the detection of solid nodules and calcified nodules between software A and manual reading, as well as between software B and pure ground glass nodules. The detection rate of nodules with the combined two kinds of AI software was 97.1%. However, compared with manual reading, there was no significant difference in the detection of nodule types. The combination of the two AI software had a good agreement with manual reading (Kappa=0.439). Conclusion: The combination of two kinds of AI software improved the ability of nodule detection and classification analysis. Furthermore, the method of joint diagnosis is recommended for clinical use and it provides evidence for further improving the homogenization management of AI data sets.