SynthesisJournal of medical Internet research2022
The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study.
Synthesis in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
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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.
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.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review.Frontiers in oncology · 2025Pooled it
- Article
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Integrative bioinformatics analyses identify EDNRA and BCL2L1 as candidate hub genes linking predicted MEHP targets and gastric cancer.Discover oncology · 2026Article
- Recent advances, comparative performance, and data requirements of artificial intelligence-assisted technologies for the diagnosis of gastric cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Evaluation of computer-aided detection for gastric cancer using white-light and linked-color imaging: a pilot study.iGIE : innovation, investigation and insights · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy.World journal of gastroenterology · 2025Review
- Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations.World journal of gastrointestinal oncology · 2025Article
- Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review.JMIR medical informatics · 2024Review
- Shedding a new light on gastric cancer screening.United European gastroenterology journal · 2024Article
- Hereditary Gastrointestinal Tumor Syndromes: When Risk Comes with Your Genes.Current issues in molecular biology · 2024Review
- [High expression of COX6B2 in gastric cancer is associated with poor long-term prognosis and promotes cell proliferation and cell cycle progression by inhibiting p53 signaling].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024Article
- Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.Clinical endoscopy · 2024Review
- Artificial intelligence in gastrointestinal endoscopy: current evidence and future directions.Therapeutic advances in gastrointestinal endoscopyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer.
objectiveThe purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images.
methodsWe conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed.
resultsWe analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results.
conclusionsFor early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation.
trial registrationPROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223.
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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.