ArticleGastroenterology2021
A Pilot Study on Automatic Three-Dimensional Quantification of Barrett's Esophagus for Risk Stratification and Therapy Monitoring.
Article in Gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed.
- 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
- Endoscopic Evaluation of the Gastroesophageal Junction and Diagnosis of Barrett's Esophagus.Digestion · 2026Review
- Associations Between Non-Genetic Risk Factors and DNA Methylation Alterations in Barrett's Esophagus and Its Progression to Esophageal Adenocarcinoma.International journal of molecular sciences · 2025Review
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- AI-Driven Risk Prediction Tool (TSP-9) Informs Risk-Aligned Care for Patients with Barrett's Esophagus.Diagnostics (Basel, Switzerland) · 2025Article
- Future of image enhanced endoscopy of esophageal adenocarcinoma.Clinical endoscopy · 2025Review
- Human-Computer Vision Collaborative Measurement of Surgical Exposure and Length in Endonasal Endoscopic Skull Base Surgery.IEEE open journal of engineering in medicine and biology · 2025Article
- Investigation of causal associations between cerebral cortical structure and Barrett's esophagus: insights from Mendelian randomization and meta-analysis.Journal of thoracic disease · 2024Article
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Adherence to quality indicators and best practices in surveillance endoscopy of Barrett's esophagus: A video-based assessment.Endoscopy international open · 2024Article
- Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions.Bioengineering (Basel, Switzerland) · 2023Review
- Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia.Clinical endoscopy · 2023Review
- Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.NPJ digital medicine · 2022Review
- Artificial Intelligence in the Management of Barrett's Esophagus and Early Esophageal Adenocarcinoma.Cancers · 2022Review
- Anatomical Engineering and 3D Printing for Surgery and Medical Devices: International Review and Future Exponential Innovations.BioMed research international · 2022Review
- Identification of Barrett's esophagus in endoscopic images using deep learning.BMC gastroenterology · 2021Article
- Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas.Cancers · 2021Review
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND &
aimsBarrett's epithelium measurement using widely accepted Prague C&M classification is highly operator dependent. We propose a novel methodology for measuring this risk score automatically. The method also enables quantification of the area of Barrett's epithelium (BEA) and islands, which was not possible before. Furthermore, it allows 3-dimensional (3D) reconstruction of the esophageal surface, enabling interactive 3D visualization. We aimed to assess the accuracy of the proposed artificial intelligence system on both phantom and endoscopic patient data.
methodsUsing advanced deep learning, a depth estimator network is used to predict endoscope camera distance from the gastric folds. By segmenting BEA and gastroesophageal junction and projecting them to the estimated mm distances, we measure C&M scores including the BEA. The derived endoscopy artificial intelligence system was tested on a purpose-built 3D printed esophagus phantom with varying BEAs and on 194 high-definition videos from 131 patients with C&M values scored by expert endoscopists.
resultsEndoscopic phantom video data demonstrated a 97.2% accuracy with a marginal ± 0.9 mm average deviation for C&M and island measurements, while for BEA we achieved 98.4% accuracy with only ±0.4 cm
conclusionsThe proposed methodology automatically extracts Prague C&M scores with high accuracy. Quantification and 3D reconstruction of the entire Barrett's area provides new opportunities for risk stratification and assessment of therapy response.
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