SynthesisAlimentary pharmacology & therapeutics2022
Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases.
Synthesis in Alimentary pharmacology & therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers, 8 of them syntheses that pooled it.
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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.
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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
35 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Esophageal and Oropharyngeal Dysphagia: Clinical Recommendations From the United European Gastroenterology and European Society for Neurogastroenterology and Motility.United European gastroenterology journal · 2025Guideline
- 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
- Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Pooled it
- The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Comparison of acid-lowering drugs for endoscopy negative reflux disease: Systematic review and network Meta-Analysis.Neurogastroenterology and motility · 2023Pooled it
- Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases.Alimentary pharmacology & therapeutics · 2022Pooled it
- Advances in the Diagnosis of Barrett's Esophagus.Diagnostics (Basel, Switzerland) · 2026Review
- Letter: Peripheral Blood Eosinophils as Complementary Biomarkers for Diagnosis and Follow-Up in Eosinophilic Oesophagitis-Authors' Reply.Alimentary pharmacology & therapeutics · 2026Article
- A novel Artificial Intelligence-assisted white light endoscopic model in the diagnosis of cardia atrophy: a multicenter study.Annals of medicine and surgery (2012) · 2026Article
- Article
- Artificial Intelligence and FLIP Panometry-Automated Classification of Esophageal Motility Patterns.Journal of clinical medicine · 2026Article
- Pathophysiology of Gastroesophageal Reflux Disease.Digestion · 2026Review
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- A deep learning-based approach to enhance accuracy and feasibility of long-term high-resolution manometry examinations.Communications medicine · 2025Article
- Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis?World journal of gastroenterology · 2025Review
- Emerging innovation, artificial intelligence, and digital technology in esophagology.Annals of the New York Academy of Sciences · 2025Review
- Challenges in Implementing Endoscopic Artificial Intelligence: The Impact of Real-World Imaging Conditions on Barrett's Neoplasia Detection.United European gastroenterology journal · 2025Article
- Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.Cancer science · 2025Article
Corrections and comments
- Erratum issuedCorrigendum.2022
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).
aimWe performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.
methodsWe searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.
resultsFor the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.
conclusionsAI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Registered trials
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.