AUTHOR=Lv Jun , Li Jianhui , Liu Yanzhen , Zhang Hong , Luo Xiangfeng , Ren Min , Gao Yufan , Ma Yanhe , Liang Shuo , Yang Yapeng , Song Zhenchun , Gao Guangming , Gao Guozheng , Jiang Yusheng , Li Ximing 

TITLE=Artificial Intelligence-Aided Diagnosis Software to Identify Highly Suspicious Pulmonary Nodules

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.749219

DOI=10.3389/fonc.2021.749219

ISSN=2234-943X

ABSTRACT=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.