Artificial Intelligence-Aided Diagnosis Software to Identify Highly Suspicious Pulmonary Nodules - Lv, Jun; Li, Jianhui; Liu, Yanzhen; Zhang, Hong; Luo, Xiangfeng; Ren, Min; Gao, Yufan; Ma, Yanhe; Liang, Shuo; Yang, Yapeng; Song, Zhenchun; Gao, Guangming; et al. (Frontiers in Oncology, 2022)
Introduction To evaluate the value of artificial intelligence (AI)-assisted software in the diagnosis of lung nodules using a combination of low-dose computed tomography (LDCT) and high-resolution computed tomography (HRCT). Method A total of 113 patients with pulmonary nodules were screened using LDCT. For nodules with the largest diameters, an HRCT local-target scanning program (combined scanning scheme) and a conventional-dose CT scanning scheme were also performed. Lung nodules were subjectively assessed for image signs and compared by size and malignancy rate measured by AI-assisted software. The nodules were divided into improved visibility and identical visibility groups based on differences in the number of signs identified through the two schemes. Results The nodule volume and malignancy probability for subsolid nodules significantly differed between the improved and identical visibility groups. For the combined scanning protocol, we observed significant between-group differences in subsolid nodule malignancy rates. Conclusion Under the operation and decision of AI, the combined scanning scheme may be beneficial for screening high-risk populations.