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Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening

Author(s):

Yaping Zhang, Beibei Jiang, Lu Zhang, Marcel J.W. Greuter, Geertruida H. de Bock, Hao Zhang and Xueqian Xie*   Pages 1 - 8 ( 8 )

Abstract:


Background: Artificial Intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of the AI system by comparing AI-assisted nodule detection with actual radiology reports.

Objective: To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening.

Methods: Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated in finalizing radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to locate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods.

Results: In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiologists observed 131, lower than 247 by AI-assisted reading (p<0.001). The panel confirmed 111 patients with >1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively.

Conclusion: Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensitivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules.

Keywords:

Artificial intelligence, lung nodule, detectability, real-world study, radiologist observation, computed tomography.

Affiliation:

Department of Radiology, Shanghai General Hospital of Nanjing Medical University, Haining Rd.100, Shanghai 200080, Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Gro-ningen, Department of Epidemiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713GZ Groningen, Department of Radiology, Shanghai General Hospital of Nanjing Medical University, Haining Rd.100, Shanghai 200080, Department of Radiology, Shanghai General Hospital of Nanjing Medical University, Haining Rd.100, Shanghai 200080



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