ArticleNature communications2024
Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025Pooled it
- Advances in the Diagnosis of Barrett's Esophagus.Diagnostics (Basel, Switzerland) · 2026Review
- Induced Molecular Orientation Transition via Organic-Inorganic Hybrid Networks in Thin-Film Transistor-Based Biosensors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Bootstrapping multimodal large language model with medical knowledge for automatic esophagogastroduodenoscopy diagnosis and reporting.Nature communications · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- The consensus molecular subtypes of esophageal squamous cell carcinoma.Signal transduction and targeted therapy · 2026Article
- TNM staging of esophageal cancer using fine-tuned pathology foundation models and multiple instance learning.Frontiers in oncology · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- AI-driven pathology in esophageal cancer: from early screening to precision prognostics.Frontiers in oncology · 2026Review
- SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.Nature cancer · 2025Article
- Swallowable expandable fibrous capsules for nonendoscopic sampling of esophageal cells.Science advances · 2025Article
- Discovery of tumour indicating morphological changes in benign prostate biopsies through AI.Scientific reports · 2025Article
- 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
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- 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
- Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.Journal of clinical medicine · 2025Review
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
Abstract
Timely detection of Barrett's esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal metaplasia in Barrett's. However, it depends on pathologist's assessment of two slides stained with H&E and the immunohistochemical biomarker TFF3. This resource-intensive clinical workflow limits large-scale screening in the at-risk population. To improve screening capacity, we propose a deep learning approach for detecting Barrett's from routinely stained H&E slides. The approach solely relies on diagnostic labels, eliminating the need for expensive localized expert annotations. We train and independently validate our approach on two clinical trial datasets, totaling 1866 patients. We achieve 91.4% and 87.3% AUROCs on discovery and external test datasets for the H&E model, comparable to the TFF3 model. Our proposed semi-automated clinical workflow can reduce pathologists' workload to 48% without sacrificing diagnostic performance, enabling pathologists to prioritize high risk cases.
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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.