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Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study
Abstract
Background: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not requiring resection, potentially reducing cost. Objective: To evaluate the performance of real-time CAD with endocytoscopes (×520 ultramagnifying colonoscopes providing microvascular and cellular visualization of colorectal polyps after application of the narrow-band imaging [NBI] and methylene blue staining modes, respectively). Design: Single-group, open-label, prospective study. (UMIN [University hospital Medical Information Network] Clinical Trial Registry: UMIN000027360). Setting: University hospital. Participants: 791 consecutive patients undergoing colonoscopy and 23 endoscopists. Intervention: Real-time use of CAD during colonoscopy. Measurements: CAD-predicted pathology (neoplastic or nonneoplastic) of detected diminutive polyps (≤5 mm) on the basis of real-time outputs compared with pathologic diagnosis of the resected specimen (gold standard). The primary end point was whether CAD with the stained mode produced a negative predictive value (NPV) of 90% or greater for identifying diminutive rectosigmoid adenomas, the threshold required to "diagnose-and-leave" nonneoplastic polyps. Best- and worst-case scenarios assumed that polyps lacking either CAD diagnosis or pathology were true- or false-positive or true- or false-negative, respectively. Results: Overall, 466 diminutive (including 250 rectosigmoid) polyps from 325 patients were assessed by CAD, with a pathologic prediction rate of 98.1% (457 of 466). The NPVs of CAD for diminutive rectosigmoid adenomas were 96.4% (95% CI, 91.8% to 98.8%) (best-case scenario) and 93.7% (CI, 88.3% to 97.1%) (worst-case scenario) with stained mode and 96.5% (CI, 92.1% to 98.9%) (best-case scenario) and 95.2% (CI, 90.3% to 98.0%) (worst-case scenario) with NBI. Limitation: Two thirds of the colonoscopies were conducted by experts who had each experienced more than 200 endocytoscopies; 186 polyps not assessed by CAD were excluded. Conclusion: Real-time CAD can achieve the performance level required for a diagnose-and-leave strategy for diminutive, nonneoplastic rectosigmoid polyps. Primary Funding Source: Japan Society for the Promotion of Science.
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“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 [27]
In the citing paper
“Computer-aided diagnosis (CADx) systems based on artificial intelligence (AI) can help endoscopists in the optical diagnosis of colorectal polyps by predicting the histopathology.”
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 [5]
In the citing paper
“The stand-alone performance of CADx systems and comparisons of CADx performances with thresholds for clinical implementation have been evaluated and show promising results.[1][2][3][4][5][6][7]”
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 [5]
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Metadata
- Authors
- Mori, Y., Kudo, S.-E., Misawa, M., Saito, Y., Ikematsu, H., Hotta, K., Ohtsuka, K., Urushibara, F., Kataoka, S., Ogawa, Y., Maeda, Y., Takeda, K., Nakamura, H., Ichimasa, K., Kudo, T., Hayashi, T., Wakamura, K., Ishida, F., Inoue, H., Itoh, H., Oda, M., & Mori, K.
- Affiliations
- SHOWA Medical University Northern Yokohama Hospital (JP)
- National Cancer Center Hospital (JP)
- National Cancer Center Hospital East (JP)
- Shizuoka Cancer Center (JP)
- Tokyo Medical and Dental University (JP)
- SHOWA Medical University Koto Toyosu Hospital (JP)
- Nagoya University (JP)
- ORCID
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2018-09-18
- Volume
- 169
- Issue
- 6
- Pages
- 357-366
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2887719255
- Cited by
- 509
- References
- 27
- Citations by year
- 2026: 26 · 2025: 53 · 2024: 45 · 2023: 68 · 2022: 65 · 2021: 98 · 2020: 101 · 2019: 43 · 2018: 9
- FWCI
- 23.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (26108006, 26108001, 17H05305, 17K15971)
- 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
- computer-aided diagnosis, colonoscopy, narrow-band imaging, methylene blue staining, negative predictive value, polypectomy, real-time image analysis, colorectal cancer screening
- MeSH terms
- Adenoma, Aged, Artificial Intelligence, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Coloring Agents, Feasibility Studies, Female, Humans, Male, Methylene Blue, Middle Aged, Prospective Studies, Sensitivity and Specificity, Narrow Band Imaging
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