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AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial
- Title
- AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial
- Authors
- Nam J.G.; Hwang E.J.; Kim J.; Park N.; Lee E.H.; Kim H.J.; Nam M.; Lee J.H.; Park C.M.; Goo J.M.
- Ewha Authors
- 김현진
- SCOPUS Author ID
- 김현진
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![scopus](/images/layout/icon2.png)
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- Issue Date
- 2023
- Journal Title
- Radiology
- ISSN
- 0033-8419
- Citation
- Radiology vol. 307, no. 2
- Publisher
- Radiological Society of North America Inc.
- Indexed
- SCIE; SCOPUS
![scopus](/images/layout/scopus2.gif)
- Document Type
- Article
- 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.
- DOI
- 10.1148/radiol.221894
- Appears in Collections:
- 의료원 > 의료원 > Journal papers
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