Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 김현진 | * |
dc.date.accessioned | 2023-07-31T16:31:16Z | - |
dc.date.available | 2023-07-31T16:31:16Z | - |
dc.date.issued | 2023 | * |
dc.identifier.issn | 0033-8419 | * |
dc.identifier.other | OAK-33511 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/265346 | - |
dc.description.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. | * |
dc.language | English | * |
dc.publisher | Radiological Society of North America Inc. | * |
dc.title | AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial | * |
dc.type | Article | * |
dc.relation.issue | 2 | * |
dc.relation.volume | 307 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.journaltitle | Radiology | * |
dc.identifier.doi | 10.1148/radiol.221894 | * |
dc.identifier.wosid | WOS:000989675600004 | * |
dc.identifier.scopusid | 2-s2.0-85150017411 | * |
dc.author.google | Nam J.G. | * |
dc.author.google | Hwang E.J. | * |
dc.author.google | Kim J. | * |
dc.author.google | Park N. | * |
dc.author.google | Lee E.H. | * |
dc.author.google | Kim H.J. | * |
dc.author.google | Nam M. | * |
dc.author.google | Lee J.H. | * |
dc.author.google | Park C.M. | * |
dc.author.google | Goo J.M. | * |
dc.contributor.scopusid | 김현진(55790161000;57205142350;57222375718;57218261214) | * |
dc.date.modifydate | 20240318142750 | * |