CoChat
Sign Up
Cited by 141Lung Cancer Diagnosis and TreatmentAI in cancer detectionRadiomics and Machine Learning in Medical Imaging

AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial

Nam, Ju Gang, Hwang, Eui Jin, Kim, Jayoun, Park, Nanhee, Lee, Eun Hee, Kim, Hyun Jin, Nam, Miyeon, Lee, Jong Hyuk +2 more·2023·Radiology
DOI 10.1148/radiol.221894
Open this paper in CoChat — free account

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 Supplemental material is available for this article. See also the editorial by Auffermann in this isssue.

Integrity signals

Registry

Crossref · 10.1148/radiol.221894

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Nam and colleagues reported from a single-center, open-label randomized controlled trial that an AI-based chest X-ray system improved detection of actionable lung nodules compared with non-AI.

    “Nam and colleagues (31) have published promising results from a singlecenter, open-label randomized control trial that utilized an AI-based system for chest X-rays from the company Lunit, and reported that the AI arm had an improved detection rate of actionable nodules compared with the non-AI arm.”

    From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [31]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

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.
Affiliations
Ewha Womans University (KR)
ORCID
Venue
Radiology
Publisher
Radiological Society of North America (RSNA)
Published
2023-04-01
Volume
307
Issue
2
Pages
e221894-e221894
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
DOI
10.1148/radiol.221894
OpenAlex
W4319333852
Cited by
141
References
26
Citations by year
2026: 34 · 2025: 52 · 2024: 35 · 2023: 20
FWCI
25.98
Type
journal article
Language
en
Open access
closed
Locations
  • Radiology (publishedVersion)
  • PubMed (publishedVersion)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
Funders
Ministry of Health and Welfare (HI19C1129)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
artificial intelligence, computer-aided detection, lung nodule detection, chest radiography, health screening, lung cancer screening, radiologist interpretation, early detection of lung cancer, positive predictive value, cost-effectiveness, health care utilization
MeSH terms
Artificial Intelligence, Humans, Lung, Lung Neoplasms, Male, Middle Aged, Precancerous Conditions, Radiographic Image Interpretation, Computer-Assisted, Radiography, Sensitivity and Specificity, Radiography, Thoracic, Tomography, X-Ray Computed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review