Literature DB >> 30105375

Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.

Yuichi Mori1, Shin-Ei Kudo1, Masashi Misawa1, Yutaka Saito2, Hiroaki Ikematsu3, Kinichi Hotta4, Kazuo Ohtsuka5, Fumihiko Urushibara1, Shinichi Kataoka1, Yushi Ogawa1, Yasuharu Maeda1, Kenichi Takeda1, Hiroki Nakamura1, Katsuro Ichimasa1, Toyoki Kudo1, Takemasa Hayashi1, Kunihiko Wakamura1, Fumio Ishida1, Haruhiro Inoue6, Hayato Itoh7, Masahiro Oda7, Kensaku Mori7.   

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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Year:  2018        PMID: 30105375     DOI: 10.7326/M18-0249

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  76 in total

1.  Human-machine collaboration: bringing artificial intelligence into colonoscopy.

Authors:  Omer F Ahmad; Danail Stoyanov; Laurence B Lovat
Journal:  Frontline Gastroenterol       Date:  2018-10-15

2.  AI in the treatment of fertility: key considerations.

Authors:  Jason Swain; Matthew Tex VerMilyea; Marcos Meseguer; Diego Ezcurra
Journal:  J Assist Reprod Genet       Date:  2020-09-29       Impact factor: 3.412

3.  Welcoming new guidelines for AI clinical research.

Authors:  Eric J Topol
Journal:  Nat Med       Date:  2020-09       Impact factor: 53.440

Review 4.  Endocytoscopy: technology and clinical application in the lower GI tract.

Authors:  Hiroyuki Takamaru; Shih Yea Sylvia Wu; Yutaka Saito
Journal:  Transl Gastroenterol Hepatol       Date:  2020-07-05

5.  UEG Week 2020 Poster Presentations.

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Journal:  United European Gastroenterol J       Date:  2020-10       Impact factor: 4.623

Review 6.  Artificial Intelligence and Polyp Detection.

Authors:  Nicholas Hoerter; Seth A Gross; Peter S Liang
Journal:  Curr Treat Options Gastroenterol       Date:  2020-01-21

7.  Introduction to deep learning: minimum essence required to launch a research.

Authors:  Tomohiro Wataya; Katsuyuki Nakanishi; Yuki Suzuki; Shoji Kido; Noriyuki Tomiyama
Journal:  Jpn J Radiol       Date:  2020-06-15       Impact factor: 2.374

8.  Validation of the European Laryngological Society classification of glottic vascular changes as seen by narrow band imaging in the optical biopsy setting.

Authors:  Francesco Missale; Stefano Taboni; Cesare Piazza; Giorgio Peretti; Andrea Luigi Camillo Carobbio; Francesco Mazzola; Giulia Berretti; Andrea Iandelli; Marco Fragale; Francesco Mora; Alberto Paderno; Francesca Del Bon; Giampiero Parrinello; Alberto Deganello
Journal:  Eur Arch Otorhinolaryngol       Date:  2021-03-12       Impact factor: 2.503

Review 9.  Artificial Intelligence in Endoscopy.

Authors:  Yutaka Okagawa; Seiichiro Abe; Masayoshi Yamada; Ichiro Oda; Yutaka Saito
Journal:  Dig Dis Sci       Date:  2021-06-21       Impact factor: 3.199

Review 10.  Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm.

Authors:  Kyeong Ok Kim; Eun Young Kim
Journal:  Gut Liver       Date:  2021-05-15       Impact factor: 4.519

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