ReviewWorld journal of gastroenterology2022
Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do.
Review in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Characteristics and causes of post-endoscopy Barrett's neoplasia: Retrospective multicenter study.Endoscopy international open · 2026Article
- Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis.World journal of gastrointestinal surgery · 2025Article
- Cyber-Physical System Interface for Implantable Esophageal Prosthesis.Sensors (Basel, Switzerland) · 2025Article
- [Telemedicine and AI-supported diagnostics in the daily routine of visceral medicine].Chirurgie (Heidelberg, Germany) · 2025Article
- Expert assessment of infiltration depth and recommendation of endoscopic resection technique in early Barrett cancer.United European gastroenterology journal · 2024Article
- Review
- What Is "Cold" and What Is "Hot" in Mucosal Ablation for Barrett's Oesophagus-Related Dysplasia: A Practical Guide.Life (Basel, Switzerland) · 2023Review
- Implementation of Artificial Intelligence-Assisted Endoscopy Across Canada-The CAG Artificial Intelligence Special Interest Group.Journal of the Canadian Association of Gastroenterology · 2023Article
- Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022)Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Barrett's esophagus (BE) is a well-established risk factor for esophageal adenocarcinoma. It is recommended that patients have regular endoscopic surveillance, with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma. Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments, and with this aim, thorough endoscopic assessment is crucial and improves outcomes. The burden of missed neoplasia in BE is still far from being negligible, likely due to inappropriate endoscopic surveillance. Over the last two decades, advanced imaging techniques, moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies, have been introduced with the aim to enhance neoplasia detection in BE. As witnessed in other fields, artificial intelligence (AI) has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well. The aim of this commentary is to comprehensively summarize present evidence, recent research advances, and future perspectives regarding advanced imaging technology and AI in BE; the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited. It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE, in order to increase the diagnostic yield of endoscopy in clinical practice.
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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.