The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Real-Time Artificial Intelligence–Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy
Abstract
BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists distinguish between neoplastic polyps requiring removal and nonneoplastic polyps not requiring removal. In this study, we tested whether CADx analyzed images helped in this decision-making process. METHODS: We performed a multicenter clinical study comparing a novel CADx-system that uses real-time ultra-magnifying polyp visualization during colonoscopy with standard visual inspection of small (≤5 mm in diameter) polyps in the sigmoid colon and the rectum for optical diagnosis of neoplastic histology. After committing to a diagnosis (i.e., neoplastic, uncertain, or nonneoplastic), all imaged polyps were removed. The primary end point was sensitivity for neoplastic polyps by CADx and visual inspection, compared with histopathology. Secondary end points were specificity and colonoscopist confidence level in unaided optical diagnosis. RESULTS: We assessed 1289 individuals for eligibility at colonoscopy centers in Norway, the United Kingdom, and Japan. We detected 892 eligible polyps in 518 patients and included them in analyses: 359 were neoplastic and 533 were nonneoplastic. Sensitivity for the diagnosis of neoplastic polyps with standard visual inspection was 88.4% (95% confidence interval [CI], 84.3 to 91.5) compared with 90.4% (95% CI, 86.8 to 93.1) with CADx (P=0.33). Specificity was 83.1% (95% CI, 79.2 to 86.4) with standard visual inspection and 85.9% (95% CI, 82.3 to 88.8) with CADx. The proportion of polyp assessment with high confidence was 74.2% (95% CI, 70.9 to 77.3) with standard visual inspection versus 92.6% (95% CI, 90.6 to 94.3) with CADx. CONCLUSIONS: Real-time polyp assessment with CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during a colonoscopy compared with optical evaluation without CADx. (Funded by the Research Council of Norway [Norges Forskningsråd], the Norwegian Cancer Society [Kreftforeningen], and the Japan Society for the Promotion of Science; UMIN number, UMIN000035213.)
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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 [33]
In the citing paper
“Furthermore, the performance of available CADx systems is comparable, if not inferior, to that of experienced endoscopists [66 , 94-96] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [94]
In the citing paper
“However, one study could not demonstrate a significant increase in sensitivity and specificity, even among non-experienced endoscopists [94]”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [94]
In the citing paper
“However, their integration into clinical practice has not consistently enhanced the diagnostic accuracy of endoscopists.14-16”
From Structured Integration of an Artificial Intelligence-Based System for the Optical Diagnosis of Colorectal Polyps (2026) · cites this paper as [14]
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Metadata
- Authors
- Barua, I., Wieszczy, P., Kudo, S.-E., Misawa, M., Holme, Ø., Gulati, S., Williams, S., Mori, K., Itoh, H., Takishima, K., Mochizuki, K., Miyata, Y., Mochida, K., Akimoto, Y., Kuroki, T., Morita, Y., Shiina, O., Kato, S., Nemoto, T., Hayee, B., Patel, M., Gunasingam, N., Kent, A., Emmanuel, A., Munck, C., Nilsen, J. A., Hvattum, S. A., Frigstad, S. O., Tandberg, P., Løberg, M., Kalager, M., Haji, A., Bretthauer, M., & Mori, Y.
- Affiliations
- Oslo University Hospital (NO)
- SHOWA Medical University Northern Yokohama Hospital (JP)
- King's College Hospital NHS Foundation Trust (GB)
- Nagoya University (JP)
- Vestre Viken Hospital Trust (NO)
- ORCID
- Barua, Ishita
- Wieszczy, Paulina
- Kudo, Shin-ei
- Misawa, Masashi
- Holme, Øyvind
- Gulati, Shraddha
- Williams, Sophie
- Mori, Kensaku
- Itoh, Hayato
- Mochizuki, Kenichi
- Akimoto, Yoshika
- Kuroki, Takanori
- Hayee, Bu
- Patel, Mehul
- Gunasingam, Nishmi
- Kent, Alexandra
- Emmanuel, Andrew
- Nilsen, Jens Aksel
- Frigstad, Svein Oskar
- Løberg, Magnus
- Kalager, Mette
- Haji, Amyn
- Bretthauer, Michael
- Mori, Yuichi
- Venue
- NEJM Evidence
- Publisher
- Massachusetts Medical Society
- Published
- 2022-05-24
- Volume
- 1
- Issue
- 6
- Pages
- EVIDoa2200003-EVIDoa2200003
- ISSN
- 2766-5526 (ISSN-L)
- OpenAlex
- W4223989602
- Cited by
- 119
- References
- 19
- Citations by year
- 2026: 16 · 2025: 36 · 2024: 30 · 2023: 28 · 2022: 8
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NEJM Evidence (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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- neoplastic polyps, computer-aided diagnosis, colonoscopy, polyp histology, sensitivity and specificity, small polyps, sigmoid colon, rectum, artificial intelligence in endoscopy, histopathology comparison, colorectal cancer screening
- MeSH terms
- Adult, Aged, Artificial Intelligence, Colonic Neoplasms, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Female, Humans, Male, Middle Aged, Sensitivity and Specificity
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