ArticleGastrointestinal endoscopy2022
Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett's esophagus.
Article in Gastrointestinal endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review.Computers in biology and medicine · 2025Pooled it
- Towards reliable morphological subtyping and cancer detection on Barrett's esophagus pathology through ensembling foundation models.NPJ digital medicine · 2026Article
- Artificial Intelligence for Esophageal Precancerous Lesions and Esophageal Cancer.The Korean journal of helicobacter and upper gastrointestinal research · 2026Review
- Review Article: Advances in the Diagnosis of Dysplasia in Barrett's Oesophagus.Alimentary pharmacology & therapeutics · 2026Review
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- TNM staging of esophageal cancer using fine-tuned pathology foundation models and multiple instance learning.Frontiers in oncology · 2026Article
- AI-Driven Risk Prediction Tool (TSP-9) Informs Risk-Aligned Care for Patients with Barrett's Esophagus.Diagnostics (Basel, Switzerland) · 2025Article
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Emerging innovation, artificial intelligence, and digital technology in esophagology.Annals of the New York Academy of Sciences · 2025Review
- Future of image enhanced endoscopy of esophageal adenocarcinoma.Clinical endoscopy · 2025Review
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Artificial Intelligence in Esophageal Diseases.Gastroenterology & hepatology · 2024Article
- YOLOv8-Coal: a coal-rock image recognition method based on improved YOLOv8.PeerJ. Computer science · 2024Article
- Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions.Bioengineering (Basel, Switzerland) · 2023Review
- Review
- Optimizing Storage and Computational Efficiency: An Efficient Algorithm for Whole Slide Image Size Reduction.Mayo Clinic proceedings. Digital health · 2023Article
- Surveillance in Barrett's Esophagus: Challenges, Progress, and Possibilities.Gastroenterology · 2023Article
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsThe risk of progression in Barrett's esophagus (BE) increases with development of dysplasia. There is a critical need to improve the diagnosis of BE dysplasia, given substantial interobserver disagreement among expert pathologists and overdiagnosis of dysplasia by community pathologists. We developed a deep learning model to predict dysplasia grade on whole-slide imaging.
methodsWe digitized nondysplastic BE (NDBE), low-grade dysplasia (LGD), and high-grade dysplasia (HGD) histology slides. Two expert pathologists confirmed all histology and digitally annotated areas of dysplasia. Training, validation, and test sets were created (by a random 70/20/10 split). We used an ensemble approach combining a "you only look once" model to identify regions of interest and histology class (NDBE, LGD, or HGD) followed by a ResNet101 model pretrained on ImageNet applied to the regions of interest. Diagnostic performance was determined for the whole slide.
resultsWe included slides from 542 patients (164 NDBE, 226 LGD, and 152 HGD) yielding 8596 bounding boxes in the training set, 1946 bounding boxes in the validation set, and 840 boxes in the test set. When the ensemble model was used, sensitivity and specificity for LGD was 81.3% and 100%, respectively, and >90% for NDBE and HGD. The overall positive predictive value and sensitivity metric (calculated as F1 score) was .91 for NDBE, .90 for LGD, and 1.0 for HGD.
conclusionsWe successfully trained and validated a deep learning model to accurately identify dysplasia on whole-slide images. This model can potentially help improve the histologic diagnosis of BE dysplasia and the appropriate application of endoscopic therapy.
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