Evidence map›Paper›PMID 32231710›Full record

ArticleTherapeutic advances in gastroenterology2020

Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks.

Tsuyoshi Ozawa, Soichiro Ishihara, Mitsuhiro Fujishiro, Youichi Kumagai, Satoki Shichijo, Tomohiro Tada

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Article in Therapeutic advances in gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tsuyoshi OzawaDepartment of Surgery, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8606, Japan.ORCID https://orcid.org/0000-0002-0701-7978
Soichiro IshiharaTada Tomohiro institute of Gastroenterology and proctology, Saitama, Japan.
Mitsuhiro FujishiroDepartment of Gastroenterology, Graduate School of Medicine, Nagoya University, Nagoya, Japan.
Youichi KumagaiDepartment of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan.
Satoki ShichijoDepartment of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan.
Tomohiro TadaTada Tomohiro institute of Gastroenterology and proctology, Saitama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently the American Society for Gastrointestinal Endoscopy addressed the 'resect and discard' strategy, determining that accurate

methodsWe used a deep convolutional neural network (CNN) architecture called Single Shot MultiBox Detector. We trained the CNN using 16,418 images from 4752 CPs and 4013 images of normal colorectums, and subsequently validated the performance of the trained CNN in 7077 colonoscopy images, including 1172 CP images from 309 various types of CP. Diagnostic speed and yields for the detection and classification of CP were evaluated as a measure of performance of the trained CNN.

resultsThe processing time of the CNN was 20 ms per frame. The trained CNN detected 1246 CP with a sensitivity of 92% and a positive predictive value (PPV) of 86%. The sensitivity and PPV were 90% and 83%, respectively, for the white light images, and 97% and 98% for the narrow band images. Among the correctly detected polyps, 83% of the CP were accurately classified through images. Furthermore, 97% of adenomas were precisely identified under the white light imaging.

conclusionsOur CNN showed promise in being able to detect and classify CP through endoscopic images, highlighting its high potential for future application as an AI-based CP diagnosis support system for colonoscopy.

Indexed as

artificial intelligenceclassificationcoloncolorectalconvolutional neural networkdetectiondiagnosispolyp

Identifiers

PMID32231710
PMCPMC7092386

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.