Computer-aided detection of malignant lung nodules on chest radiographs: effect on observers’ performance
Open accessAbstract
OBJECTIVE: To evaluate the effect of computer-aided detection (CAD) system on observer performance in the detection of malignant lung nodules on chest radiograph. MATERIALS AND METHODS: Two hundred chest radiographs (100 normal and 100 abnormal with malignant solitary lung nodules) were evaluated. With CT and histological confirmation serving as a reference, the mean nodule size was 15.4 mm (range, 7-20 mm). Five chest radiologists and five radiology residents independently interpreted both the original radiographs and CAD output images using the sequential testing method. The performances of the observers for the detection of malignant nodules with and without CAD were compared using the jackknife free-response receiver operating characteristic analysis. RESULTS: Fifty-nine nodules were detected by the CAD system with a false positive rate of 1.9 nodules per case. The detection of malignant lung nodules significantly increased from 0.90 to 0.92 for a group of observers, excluding one first-year resident (p = 0.04). When lowering the confidence score was not allowed, the average figure of merit also increased from 0.90 to 0.91 (p = 0.04) for all observers after a CAD review. On average, the sensitivities with and without CAD were 87% and 84%, respectively; the false positive rates per case with and without CAD were 0.19 and 0.17, respectively. The number of additional malignancies detected following true positive CAD marks ranged from zero to seven for the various observers. CONCLUSION: The CAD system may help improve observer performance in detecting malignant lung nodules on chest radiographs and contribute to a decrease in missed lung cancer.
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“whereas overreliance may lead to blind trust (overtrust) or overutilization. 11,12”
From Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction (2024) · cites this paper as [12]
The Results section (page unavailable) reports that one resident rejected many true nodules CAD did not detect and that readers accepted false-positive CAD marks. These findings support a concern about trusting CAD too much, but the paper does not address overutilization as a consequence of overreliance. (LLM, conf=0.78)
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XAI-based Clinical Decision Support System: A Systematic Review
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- Authors
- Lee, K. H., Goo, J. M., Park, C. M., Lee, H. J., & Jin, K. N.
- Affiliations
- Seoul National University (KR)
- Corresponding author
- Goo, Jin Mo
- Venue
- Korean Journal of Radiology
- Publisher
- The Korean Society of Radiology
- Published
- 2012
- Volume
- 13
- Issue
- 5
- Pages
- 564
- ISSN
- 1229-6929 (ISSN-L)
- 2005-8330
- PMC
- PMC3435853
- OpenAlex
- W2139150365
- Cited by
- 37
- References
- 0
- Citations by year
- 2024: 3 · 2023: 4 · 2022: 2 · 2021: 10 · 2020: 4 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 2 · 2015: 2 · 2014: 3 · 2013: 1
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Korean Journal of Radiology (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Ministry of Knowledge Economy
- 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
- computer-aided detection, chest radiograph, observer performance, lung cancer detection, false positive rate, sensitivity, radiologist interpretation, sequential testing
- MeSH terms
- Aged, Algorithms, Diagnosis, Computer-Assisted, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Lung Neoplasms, Male, Middle Aged, ROC Curve, Radiography, Thoracic, Tomography, X-Ray Computed, Reproducibility of Results, Observer Variation
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