Literature DB >> 34363763

Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis.

Marco Spadaccini1, Andrea Iannone2, Roberta Maselli3, Matteo Badalamenti4, Madhav Desai5, Viveksandeep Thoguluva Chandrasekar6, Harsh K Patel7, Alessandro Fugazza4, Gaia Pellegatta4, Piera Alessia Galtieri4, Gianluca Lollo8, Silvia Carrara4, Andrea Anderloni4, Douglas K Rex9, Victor Savevski10, Michael B Wallace11, Pradeep Bhandari12, Thomas Roesch13, Ian M Gralnek14, Prateek Sharma5, Cesare Hassan15, Alessandro Repici3.   

Abstract

BACKGROUND: Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imaging techniques and distal attachment devices is unclear.
METHODS: For this systematic review and network meta-analysis, we did a comprehensive search of PubMed/Medline, Embase, and Scopus databases from inception to Nov 30, 2020, for randomised controlled trials investigating the effectiveness of the following endoscopic techniques in detecting colorectal neoplasia: CADe, high definition (HD) white-light endoscopy, chromoendoscopy, or add-on devices (ie, systems that increase mucosal visualisation, such as full spectrum endoscopy [FUSE] or G-EYE balloon endoscopy). We collected data on adenoma detection rates, sessile serrated lesion detection rates, the proportion of large adenomas detected per colonoscopy, and withdrawal times. A frequentist framework, random-effects network meta-analysis was done to compare artificial intelligence with chromoendoscopy, increased mucosal visualisation systems, and HD white-light endoscopy (the control group). We estimated odds ratios (ORs) for the adenoma detection rate, sessile serrated lesion detection rate, and proportion of large adenomas detected per colonoscopy, and calculated mean differences for withdrawal time, with 95% CIs. Risk of bias and certainty of evidence were assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.
FINDINGS: 50 randomised controlled trials, comprising 34 445 participants, were included in our main analysis (six trials of CADe, 18 of chromoendoscopy, and 26 of increased mucosal visualisation systems). HD white-light endoscopy was the control technique in all 50 studies. Compared with the control technique, the adenoma detection rate was 7·4% higher with CADe (OR 1·78 [95% CI 1·44-2·18]), 4·4% higher with chromoendoscopy (1·22 [1·08-1·39]), and 4·1% higher with increased mucosal visualisation systems (1·16 [1·04-1·28]). CADe ranked as the superior technique for adenoma detection (with moderate confidence in hierarchical ranking); cross-comparisons of CADe with other imaging techniques showed a significant increase in the adenoma detection rate with CADe versus increased mucosal visualisation systems (OR 1·54 [95% CI 1·22-1·94]; low certainty of evidence) and with CADe versus chromoendoscopy (1·45 [1·14-1·85]; moderate certainty of evidence). When focusing on large adenomas (≥10 mm) there was a significant increase in the detection of large adenomas only with CADe (OR 1·69 [95% CI 1·10-2·60], moderate certainty of evidence) when compared to HD white-light endoscopy; CADe ranked as the superior strategy for detection of large adenomas. CADe also seemed to be the superior strategy for detection of sessile serrated lesions (with moderate confidence in hierarchical ranking), although no significant increase in the sessile serrated lesion detection rate was shown (OR 1·37 [95% CI 0·65-2·88]). No significant difference in withdrawal time was reported for CADe compared with the other techniques.
INTERPRETATION: Based on the published literature, detection rates of colorectal neoplasia are higher with CADe than with other techniques such as chromoendoscopy or tools that increase mucosal visualisation, supporting wider incorporation of CADe strategies into community endoscopy services. FUNDING: None.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Year:  2021        PMID: 34363763     DOI: 10.1016/S2468-1253(21)00215-6

Source DB:  PubMed          Journal:  Lancet Gastroenterol Hepatol


  6 in total

Review 1.  Highlighting the Undetectable - Fluorescence Molecular Imaging in Gastrointestinal Endoscopy.

Authors:  Judith A Stibbe; Petra Hoogland; Friso B Achterberg; Derek R Holman; Raoul S Sojwal; Jacobus Burggraaf; Alexander L Vahrmeijer; Wouter B Nagengast; Stephan Rogalla
Journal:  Mol Imaging Biol       Date:  2022-06-28       Impact factor: 3.488

Review 2.  Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications.

Authors:  James Weiquan Li; Lai Mun Wang; Tiing Leong Ang
Journal:  Singapore Med J       Date:  2022-03       Impact factor: 3.331

Review 3.  Artificial Intelligence in Digestive Endoscopy-Where Are We and Where Are We Going?

Authors:  Radu-Alexandru Vulpoi; Mihaela Luca; Adrian Ciobanu; Andrei Olteanu; Oana-Bogdana Barboi; Vasile Liviu Drug
Journal:  Diagnostics (Basel)       Date:  2022-04-08

Review 4.  Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do.

Authors:  Marco Spadaccini; Edoardo Vespa; Viveksandeep Thoguluva Chandrasekar; Madhav Desai; Harsh K Patel; Roberta Maselli; Alessandro Fugazza; Silvia Carrara; Andrea Anderloni; Gianluca Franchellucci; Alessandro De Marco; Cesare Hassan; Pradeep Bhandari; Prateek Sharma; Alessandro Repici
Journal:  World J Gastroenterol       Date:  2022-03-21       Impact factor: 5.742

5.  The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze.

Authors:  Joel Troya; Daniel Fitting; Markus Brand; Boban Sudarevic; Jakob Nikolas Kather; Alexander Meining; Alexander Hann
Journal:  Endoscopy       Date:  2022-02-14       Impact factor: 9.776

6.  Real-time colorectal polyp detection using a novel computer-aided detection system (CADe): a feasibility study.

Authors:  E Soons; T Rath; Y Hazewinkel; W A van Dop; D Esposito; P A Testoni; P D Siersema
Journal:  Int J Colorectal Dis       Date:  2022-09-27       Impact factor: 2.796

  6 in total

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