Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis
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Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp (CP) significantly improve the prevention of colorectal cancer (CRC). The current effectiveness of the diagnostic performance of colonoscopy remains unsatisfactory with unstable accuracy. The convolutional neural networks (CNN) system based on artificial intelligence (AI) technology has demonstrated its potential to help endoscopists in increasing diagnostic accuracy. Nonetheless, several limitations of the CNN system and controversies exist on whether it provides a better diagnostic performance compared to human endoscopists. Therefore, this study sought to address this issue. Online databases (PubMed, Web of Science, Cochrane Library, and EMBASE) were used to search for studies conducted up to April 2020. Besides, the quality assessment of diagnostic accuracy scale-2 (QUADAS-2) was used to evaluate the quality of the enrolled studies. Moreover, publication bias was determined using the Deeks’ funnel plot. In total, 13 studies were enrolled for this meta-analysis (ranged between 2016 and 2020). Consequently, the CNN system had a satisfactory diagnostic performance in the field of CP detection (sensitivity: 0.848 [95% CI: 0.692–0.932]; specificity: 0.965 [95% CI: 0.946–0.977]; and AUC: 0.98 [95% CI: 0.96–0.99]) and CP classification (sensitivity: 0.943 [95% CI: 0.927–0.955]; specificity: 0.894 [95% CI: 0.631–0.977]; and AUC: 0.95 [95% CI: 0.93–0.97]). In comparison with human endoscopists, the CNN system was comparable to the expert but significantly better than the non-expert in the field of CP classification (CNN vs. expert: RDOR: 1.03, P = 0.9654; non-expert vs. expert: RDOR: 0.29, P = 0.0559; non-expert vs. CNN: 0.18, P = 0.0342). Therefore, the CNN system exhibited a satisfactory diagnostic performance for CP and could be used as a potential clinical diagnostic tool during colonoscopy.
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Previous studies on CADx systems for colonoscopy focused on assessing the performances of AI against the accuracy of physicians or against criteria established by gastroenterology societies for implementing the technology in a clinical setting.
“Previous studies on cadx systems for colonoscopy focused on assessing the performances of ai against the accuracy of physicians19,20,27–30, or against the criteria established by gastroenterology societies for implementing the technology in a clinical setting31–34.”
From Experimental evidence of effective human–AI collaboration in medical decision-making (2022) · cites this paper as [29]
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- Authors
- Xu, Y., Ding, W., Wang, Y., Tan, Y., Xi, C., Ye, N., Wu, D., & Xu, X.
- Affiliations
- Jiangsu University (CN)
- Jiangsu Provincial Hospital of Traditional Chinese Medicine (CN)
- Corresponding author
- Xu, Xuezhong
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-02-16
- Volume
- 16
- Issue
- 2
- Pages
- e0246892
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7886136
- OpenAlex
- W3129241924
- Cited by
- 42
- References
- 44
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 12 · 2022: 11 · 2021: 3
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- colorectal polyps, convolutional neural networks, artificial intelligence in colonoscopy, polyp classification, polyp detection, diagnostic accuracy, colorectal cancer prevention, computer-aided diagnosis, colonoscopy quality assurance, QUADAS-2, sensitivity and specificity, area under the curve
- MeSH terms
- Colonic Polyps, Humans, Endoscopy, Digestive System, Neural Networks, Computer
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 ›
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