ArticleScientific reports2024
Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement.Endoscopy · 2026Trial
- Towards a neuro-symbolic approach for precision anti-reflux surgery.Updates in surgery · 2026Review
- Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.NPJ digital medicine · 2026Article
- Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping.Langenbeck's archives of surgery · 2026Article
- A Trustable Spine Abnormalities Classification System Using ResNet50 and VGG16 Supported by Explainable Artificial Intelligence.Biomimetics (Basel, Switzerland) · 2026Article
- The "ABCD" framework for assessment: a task-oriented approach to reconcile multimodal diagnostic discordance.Frontiers in medicine · 2026Review
- Artificial intelligence in gastroenterology: Ethical and diagnostic challenges in clinical practice.World journal of gastroenterology · 2025Article
- Prospective Evaluation of Real-Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction.United European gastroenterology journal · 2025Article
- Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.PloS one · 2025Article
- Hiatal Hernias Revisited-A Systematic Review of Definitions, Classifications, and Applications.Life (Basel, Switzerland) · 2024Review
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Authors and funding
7 authors.
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Abstract
Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.
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