AUTHORS
Ju Gang Nam, Eui Jin Hwang, Jayoun Kim, Nanhee Park,
Eun Hee Lee, Hyun Jin Kim, Miyeon Nam, Jong Hyuk Lee,
Chang Min Park, Jin Mo Goo
From the Department of Radiology (J.G.N., E.J.H., J.H.L., C.M.P., J.M.G.), Artificial Intelligence Collaborative Network (J.G.N.), Medical Research Collaborating Center (J.K., N.P.), and Center for Health Promotion and Optimal Aging (E.H.L., M.N.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea (H.J.K.); Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, Seoul, Republic of Korea (C.M.P.); and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (J.M.G.).
PUBLISHED
Abstract
Background
The impact of artificial intelligence (AI)–based computer-aided detection (CAD) software has not been prospectively explored in real-world populations.
Purpose
To investigate whether commercial AI-based CAD software could improve the detection rate of actionable lung nodules on chest radiographs in participants undergoing health checkups.
Materials and Methods
In this single-center, pragmatic, open-label randomized controlled trial, participants who underwent chest radiography between July 2020 and December 2021 in a health screening center were enrolled and randomized into intervention (AI group) and control (non-AI group) arms. One of three designated radiologists with 13–36 years of experience interpreted each radiograph, referring to the AI-based CAD results for the AI group. The primary outcome was the detection rate, that is, the number of true-positive radiographs divided by the total number of radiographs, of actionable lung nodules confirmed on CT scans obtained within 3 months. Actionable nodules were defined as solid nodules larger than 8 mm or subsolid nodules with a solid portion larger than 6 mm (Lung Imaging Reporting and Data System, or Lung-RADS, category 4). Secondary outcomes included the positive-report rate, sensitivity, false-referral rate, and malignant lung nodule detection rate. Clinical outcomes were compared between the two groups using univariable logistic regression analyses.
Results
A total of 10 476 participants (median age, 59 years [IQR, 50–66 years]; 5121 men) were randomized to an AI group (n = 5238) or non-AI group (n = 5238). The trial met the predefined primary outcome, demonstrating an improved detection rate of actionable nodules in the AI group compared with the non-AI group (0.59% [31 of 5238 participants] vs 0.25% [13 of 5238 participants], respectively; odds ratio, 2.4; 95% CI: 1.3, 4.7; P = .008). The detection rate for malignant lung nodules was higher in the AI group compared with the non-AI group (0.15% [eight of 5238 participants] vs 0.0% [0 of 5238 participants], respectively; P = .008). The AI and non-AI groups showed similar false-referral rates (45.9% [56 of 122 participants] vs 56.0% [56 of 100 participants], respectively; P = .14) and positive-report rates (2.3% [122 of 5238 participants] vs 1.9% [100 of 5238 participants]; P = .14).
Conclusion
In health checkup participants, artificial intelligence-based software improved the detection of actionable lung nodules on chest radiographs.
© RSNA, 2023