Deep learning for colorectal polyp detection: time for clinical implementation?
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Most of those studies also found increased detection of diminutive adenomas (≤5 mm), which are very rarely malignant.
“However, most of these same studies also found an increased detection rate of diminutive adenomas (defined as polyps ≤5 mm in size), which are very rarely malignant (23,24).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [23]
Cites
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Self-citationExtension of the CONSORT and SPIRIT statements
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Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
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- Authors
- Ahmad, O. F.
- Affiliations
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences (GB)
- Corresponding author
- Ahmad, Omer F
- ORCID
- Ahmad, Omer F
- Venue
- The Lancet Gastroenterology & Hepatology
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 5
- Issue
- 4
- Pages
- 330-331
- ISSN
- 2468-1156 (ISSN-L)
- 2468-1253
- OpenAlex
- W3001061830
- Cited by
- 9
- References
- 9
- Citations by year
- 2025: 2 · 2024: 4 · 2022: 3
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet. Gastroenterology & hepatology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
- Keywords
- colorectal polyp detection, computer-aided detection, deep learning, colonoscopy, adenoma detection, interval colorectal cancer, clinical implementation, colorectal cancer prevention
- MeSH terms
- Deep Learning, Adenoma, Colonic Polyps, Colonoscopy, Humans, Prospective Studies
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