Evidence map›Paper›PMID 29399610›Full record

ArticleEndoscopy international open2018

Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images.

Takumi Itoh, Hiroshi Kawahira, Hirotaka Nakashima, Noriko Yata

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Endoscopy international open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed, 4 pooled it
23.9field-weighted citation impact, top 1% of its field
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

74 citing papers in PubMed, 4 syntheses or guidelines pooled it, 189 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020
    Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Limited discriminative performance of endoscopic deep learning forFrontiers in gastroenterology (Lausanne, Switzerland) · 2026
    Article
  8. Article
  9. Review
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  11. Role of Artificial Intelligence in Endoscopic Intervention: A Clinical Review.Journal of community hospital internal medicine perspectives · 2024
    Review
  12. Review
  13. Diagnostic usefulness of deep learning methods forJGH open : an open access journal of gastroenterology and hepatology · 2023
    Article
  14. Article
  15. Review
  16. Article
  17. Article
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  20. [Clinical Application of the Kyoto Classification of Gastritis].The Korean journal of helicobacter and upper gastrointestinal research · 2023
    Review

14 more citing papers are in PubMed but not listed here.

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

4 authors at 1 institution in 1 country.

Takumi ItohDepartment of Medical System Engineering, Graduate School of Engineering, Chiba University.
Hiroshi KawahiraCenter for Frontier Medical Engineering, Chiba University, Chiba, Japan.
Hirotaka NakashimaDepartment of Gastroenterology, Foundation for Detection of Early Gastric Carcinoma, Tokyo, Japan.
Noriko YataDepartment of Information Processing and Computer Science, Graduate School of Advanced Integration Science, Chiba University, Chiba, Japan.
Chiba University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND STUDY

aimsHelicobacter pylori (HP)-associated chronic gastritis can cause mucosal atrophy and intestinal metaplasia, both of which increase the risk of gastric cancer. The accurate diagnosis of HP infection during routine medical checks is important. We aimed to develop a convolutional neural network (CNN), which is a machine-learning algorithm similar to deep learning, capable of recognizing specific features of gastric endoscopy images. The goal behind developing such a system was to detect HP infection early, thus preventing gastric cancer. PATIENTS AND

methodsFor the development of the CNN, we used 179 upper gastrointestinal endoscopy images obtained from 139 patients (65 were HP-positive: ≥ 10 U/mL and 74 were HP-negative: < 3 U/mL on HP IgG antibody assessment). Of the 179 images, 149 were used as training images, and the remaining 30 (15 from HP-negative patients and 15 from HP-positive patients) were set aside to be used as test images. The 149 training images were subjected to data augmentation, which yielded 596 images. We used the CNN to create a learning tool that would recognize HP infection and assessed the decision accuracy of the CNN with the 30 test images by calculating the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC).

resultsThe sensitivity and specificity of the CNN for the detection of HP infection were 86.7 % and 86.7 %, respectively, and the AUC was 0.956.

conclusionsCNN-aided diagnosis of HP infection seems feasible and is expected to facilitate and improve diagnosis during health check-ups.

Identifiers

PMID29399610
PMCPMC5794437
OpenAlexW2785825652

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.