Evidence map›Paper›PMID 35756602›Full record

SynthesisFrontiers in oncology2022

Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis.

De Luo, Fei Kuang, Juan Du, Mengjia Zhou, Xiangdong Liu, Xinchen Luo, Yong Tang, Bo Li, Song Su

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

Synthesis in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 4 pooled it
5.2field-weighted citation impact, top 4% 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

20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026
    Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025
    Review
  12. Article
  13. Article
  14. Screening for and surveillance of premalignant conditions of the stomach.Best practice & research. Clinical gastroenterology · 2025
    Review
  15. Review
  16. Review
  17. Review
  18. Review
  19. Article
  20. Review
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

9 authors at 6 institutions in 1 country.

De LuoDepartment of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Fei KuangDepartment of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China.
Juan DuDepartment of Clinical Medicine, Southwest Medical University, Luzhou, China.
Mengjia ZhouDepartment of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Xiangdong LiuDepartment of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China.
Xinchen LuoDepartment of Gastroenterology, Zigong Third People's Hospital, Zigong, China.
Yong TangSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Bo LiDepartment of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Song SuDepartment of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Affiliated Hospital of Southwest Medical University · CNZigong First People's Hospital · CNChanghai Hospital · CNShanghai University of Traditional Chinese Medicine · CNSouthwest Medical University · CNUniversity of Electronic Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods: Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results: Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95-0.96), 0.95 (95% CI, 0.94-0.95), 10.76 (95% CI, 7.33-15.79), 0.07 (95% CI, 0.04-0.11), and 173.93 (95% CI, 81.79-369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91-0.96), 0.90 (95% CI, 0.88-0.92), 6.14 (95% CI, 2.06-18.30), 0.07 (95% CI, 0.04-0.11), and 69.13 (95% CI, 14.73-324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87-0.88), 0.88 (95% CI, 0.87-0.88), 7.20 (95% CI, 4.32-12.00), 0.14 (95% CI, 0.09-0.23), and 48.77 (95% CI, 24.98-95.19), respectively. Conclusions: On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

Indexed as

artificial intelligenceearly detection of cancerendoscopysystematic reviewupper gastrointestinal tract

Identifiers

PMID35756602
PMCPMC9229174
OpenAlexW4282033832

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.