AccScience Publishing / TD / Online First / DOI: 10.36922/td.4178
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ORIGINAL RESEARCH ARTICLE

Evaluating the effectiveness of artificial intelligence imaging in the qualitative diagnosis of pulmonary nodules

Chunlan Hu1 ,  Dan Yang1 ,  Xiangwen Luo1 ,  Chao Lv1 ,  Juan Li1 ,  Yaya Zhang1 ,  Xinrong Xiong1 ,  Long Zhou2*
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1 Cancer Early Diagnosis and Treatment Center, Clinical Research Center, Medical Pathology Center, and Translational Medicine Research Center, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, China
2 Department of Laboratory Medicine, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, China
Tumor Discovery 2024, 3(3), 4178 https://doi.org/10.36922/td.4178
Submitted: 9 July 2024 | Accepted: 2 September 2024 | Published: 25 September 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Our study aimed to evaluate the effectiveness of artificial intelligence (AI) image diagnostic systems in the qualitative diagnosis of pulmonary nodules. We analyzed 291 cases from June 2023 to January 2024 at Chongqing University Three Gorges Hospital. All patients in the study underwent low-dose chest computed tomography scans, which identified lung nodules, followed by thoracic surgery for pathological confirmation. We compared the predictive accuracy of AI-based diagnosis with that of physician-based diagnosis in distinguishing between benign and malignant lung nodules. Among the 291 lung nodules examined, 226 were cancerous, and 65 were benign. Receiver operating characteristic (ROC) curves, plotted based on the malignancy probabilities predicted by both methods, revealed that the AI group achieved an area under the ROC curve (AUC) of 0.727, with a sensitivity of 90.27% and a specificity of 58.46%. In comparison, the physician-reading group had an AUC of 0.737, with a sensitivity of 83.19% and a specificity of 66.15%. Our findings demonstrate that the AI diagnostic system effectively calculates malignancy probabilities for lung nodules, highlighting its significant predictive potential. This system can serve as a valuable adjunct tool for clinicians and imaging physicians in the diagnostic process.

Keywords
Artificial intelligence
Pulmonary nodule
Assisted diagnosis
Funding
This study is jointly supported by the Wanzhou District Science and Health Joint Medical Research Program (wzstc-kw2023004, wzstc-kw2023039).
Conflict of interest
The authors declare that they have no competing interests.
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Tumor Discovery, Electronic ISSN: 2810-9775 Print ISSN: 3060-8597, Published by AccScience Publishing
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