ArticleWorld journal of gastroenterology2023
Artificial intelligence system for the detection of Barrett's esophagus.
Article in World journal of gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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
16 citing papers in PubMed, 21 citations in OpenAlex.
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping.Langenbeck's archives of surgery · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- TNM staging of esophageal cancer using fine-tuned pathology foundation models and multiple instance learning.Frontiers in oncology · 2026Article
- Nonendoscopic Screening for Barrett's Esophagus: Current Methods, Evidence, and Future Directions.Gastro hep advances · 2026Review
- The role of artificial intelligence in gastroenterology: current perspectives and future directions-narrative review.Translational gastroenterology and hepatology · 2026Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- A YOLO Ensemble Framework for Detection of Barrett's Esophagus Lesions in Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- Role of Artificial Intelligence in Gastroenterology Training (2005-2025): Trends, Tools, and Challenges.Cureus · 2025Review
- Precision Care in Screening, Surveillance, and Overall Management of Barrett's Esophagus.Journal of personalized medicine · 2025Review
- Future of image enhanced endoscopy of esophageal adenocarcinoma.Clinical endoscopy · 2025Review
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.World journal of gastroenterology · 2025Article
- Gender disparities and woman-specific trends in Barrett's esophagus in the United States: An 11-year nationwide population-based study.World journal of methodology · 2025Article
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Advancements in Barrett's esophagus detection: The role of artificial intelligence and its implications.World journal of gastroenterology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBarrett's esophagus (BE), which has increased in prevalence worldwide, is a precursor for esophageal adenocarcinoma. Although there is a gap in the detection rates between endoscopic BE and histological BE in current research, we trained our artificial intelligence (AI) system with images of endoscopic BE and tested the system with images of histological BE.
aimTo assess whether an AI system can aid in the detection of BE in our setting.
methodsEndoscopic narrow-band imaging (NBI) was collected from Chung Shan Medical University Hospital and Changhua Christian Hospital, resulting in 724 cases, with 86 patients having pathological results. Three senior endoscopists, who were instructing physicians of the Digestive Endoscopy Society of Taiwan, independently annotated the images in the development set to determine whether each image was classified as an endoscopic BE. The test set consisted of 160 endoscopic images of 86 cases with histological results.
resultsSix pre-trained models were compared, and EfficientNetV2B2 (accuracy [ACC]: 0.8) was selected as the backbone architecture for further evaluation due to better ACC results. In the final test, the AI system correctly identified 66 of 70 cases of BE and 85 of 90 cases without BE, resulting in an ACC of 94.37%.
conclusionOur AI system, which was trained by NBI of endoscopic BE, can adequately predict endoscopic images of histological BE. The ACC, sensitivity, and specificity are 94.37%, 94.29%, and 94.44%, respectively.
Indexed as
Identifiers
What OpenQuestion holds
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.