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Cited by 54Colorectal Cancer Screening and DetectionAI in cancer detectionOptical Imaging and Spectroscopy Techniques

A novel AI device for real-time optical characterization of colorectal polyps

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Biffi, Carlo, Salvagnini, Pietro, Dinh, Nhan Ngo, Hassan, Cesare, Sharma, Prateek, GI Genius CADx Study Group, Antonelli, Giulio, Awadie, Halim +25 more·2022·npj Digital Medicine
DOI 10.1038/s41746-022-00633-6PMC PMC9247164
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Abstract

Accurate in-vivo optical characterization of colorectal polyps is key to select the optimal treatment regimen during colonoscopy. However, reported accuracies vary widely among endoscopists. We developed a novel intelligent medical device able to seamlessly operate in real-time using conventional white light (WL) endoscopy video stream without virtual chromoendoscopy (blue light, BL). In this work, we evaluated the standalone performance of this computer-aided diagnosis device (CADx) on a prospectively acquired dataset of unaltered colonoscopy videos. An international group of endoscopists performed optical characterization of each polyp acquired in a prospective study, blinded to both histology and CADx result, by means of an online platform enabling careful video assessment. Colorectal polyps were categorized by reviewers, subdivided into 10 experts and 11 non-experts endoscopists, and by the CADx as either “adenoma” or “non-adenoma”. A total of 513 polyps from 165 patients were assessed. CADx accuracy in WL was found comparable to the accuracy of expert endoscopists (CADx WL/Exp; OR 1.211 [0.766–1.915]) using histopathology as the reference standard. Moreover, CADx accuracy in WL was found superior to the accuracy of non-expert endoscopists (CADx WL/NonExp; OR 1.875 [1.191–2.953]), and CADx accuracy in BL was found comparable to it (CADx BL/CADx WL; OR 0.886 [0.612–1.282]). The proposed intelligent device shows the potential to support non-expert endoscopists in systematically reaching the performances of expert endoscopists in optical characterization.

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  • The stand-alone performance of CADx systems and comparisons of CADx performances with thresholds for clinical implementation have been evaluated and show promising results.

    “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 [4]

    The Results (Performance comparisons; page unavailable) report promising stand-alone CADx performance: white-light accuracy was non-inferior to expert endoscopists and superior to non-experts. The Methods (Statistical analysis; page unavailable) specify thresholds for those study comparisons, but our copy does not establish that CADx performance was compared with thresholds for clinical implementation. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Computer-aided diagnosis (CADx) systems based on artificial intelligence (AI) can help endoscopists in the optical diagnosis of colorectal polyps by predicting the histopathology.

    “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 [4]

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Cited by

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Authors
Biffi, C., Salvagnini, P., Dinh, N. N., Hassan, C., Sharma, P., GI Genius CADx Study Group, Antonelli, G., Awadie, H., Bernhofer, S., Carballal, S., Dinis-Ribeiro, M., Fernández-Clotet, A., Esparrach, G. F., Gralnek, I., Higasa, Y., Hirabayashi, T., Hirai, T., Iwatate, M., Kawano, M., Mader, M., Maieron, A., Mattes, S., Nakai, T., Ordas, I., Ortigão, R., Zúñiga, O. O., Pellisé, M., Pinto, C., Riedl, F., Sánchez, A., Steiner, E., Tanaka, Y., & Cherubini, A.
Affiliations
  • Humanitas University (IT)
  • University of Kansas (US)
  • Emek Medical Center (IL)
  • Universitätsklinikum St. Pölten (AT)
  • IPO Porto (PT)
  • Hyogo Prefectural Cancer Center (JP)
  • Sugita Genpaku Memorial Obama Municipal Hospital (JP)
  • Japanese Red Cross Kobe Hospital (JP)
  • Kobe University Hospital (JP)
  • Hospital Clínic de Barcelona (ES)
  • University of Milano-Bicocca (IT)
Corresponding author
Cherubini, Andrea
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2022-06-30
Volume
5
Issue
1
Pages
84-84
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-022-00633-6
PMC
PMC9247164
OpenAlex
W4283751901
Cited by
54
References
1
Citations by year
2026: 11 · 2025: 11 · 2024: 10 · 2023: 17 · 2022: 5
FWCI
4.46
Type
journal article
Language
en
Update notices
correction (2022-08-16)
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
Keywords
colorectal polyps, computer-aided diagnosis, white light endoscopy, virtual chromoendoscopy, optical characterization, adenoma classification, colonoscopy, real-time image analysis

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