Evidence map›Paper›PMID 34920705›Full record

ArticleBMC gastroenterology2021

Identification of Barrett's esophagus in endoscopic images using deep learning.

Wen Pan, Xujia Li, Weijia Wang, Linjing Zhou, Jiali Wu, Tao Ren, Chao Liu, Muhan Lv, Song Su, Yong Tang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Diagnosis and Management of Barrett's Esophagus.Journal of clinical medicine · 2023
    Review
  9. Review
  10. Review
  11. Article
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

10 authors.

Wen Pan *Department of Digestion, West China Hospital of Sichuan University, Chengdu, 610054, Sichuan, China.
Xujia Li *Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China.
Weijia Wang *School of Information and Software Engineering, University of Electronic Science and Technology of China, 4 North Jianshe Road, Chengdu, 610054, Sichuan, China.
Linjing ZhouSchool of Information and Software Engineering, University of Electronic Science and Technology of China, 4 North Jianshe Road, Chengdu, 610054, Sichuan, China.
Jiali WuDepartment of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China.
Tao RenDepartment of Digestion, The Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Ximianqiao Street No.20, Chengdu, 610054, Sichuan, China.
Chao LiuDepartment of Digestion, The Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region, Ximianqiao Street No.20, Chengdu, 610054, Sichuan, China. liuchao-delta@126.com.
Muhan LvDepartment of Digestion, The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China. lvmuhan@swmu.edu.cn.
Song SuDepartment of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Taiping Street No.25, Luzhou, 646000, Sichuan, China. 13882778554@163.com.
Yong TangSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, 4 North Jianshe Road, Chengdu, 610054, Sichuan, China. tangyong@uestc.edu.cn.

Funding

Natural Science Foundation of Tibet Autonomous Region XZ2019ZR G-130The Applied Basic Research Project of Science & Technology Department of Luzhou city 2018-JYJ-45the Innovation Method Program of the Ministry of Science and Technology of the People's Republic of China M112017IM010700The Key Research and Development Project of Science & Technology Department of Sichuan Province 20ZDYF1129
6 · The paper itself

Abstract

backgroundDevelopment of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.

methods443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU).

resultsThe deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively.

conclusionsDeep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.

Indexed as

Barrett EsophagusDeep LearningAlgorithmsEndoscopyEsophagogastric JunctionHumansBarrett's esophagusDeep learningEsophagoscopeFully convolutional networksSegmentation

Identifiers

PMID34920705
PMCPMC8684213

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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.