ArticleUnited European gastroenterology journal2023
Towards a robust and compact deep learning system for primary detection of early Barrett's neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set.
Article in United European gastroenterology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 37 citations in OpenAlex.
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Evaluation of an improved computer-aided detection system for Barrett's neoplasia in real-world imaging conditions.Endoscopy · 2025Article
- Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 2025Review
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Challenges in Implementing Endoscopic Artificial Intelligence: The Impact of Real-World Imaging Conditions on Barrett's Neoplasia Detection.United European gastroenterology journal · 2025Article
- Future of image enhanced endoscopy of esophageal adenocarcinoma.Clinical endoscopy · 2025Review
- The development and ex vivo evaluation of a computer-aided quality control system for Barrett's esophagus endoscopy.Endoscopy · 2025Article
- Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.Endoscopy · 2025Article
- Development and validation of the MRI-based deep learning classifier for distinguishing perianal fistulizing Crohn's disease from cryptoglandular fistula: a multicenter cohort study.EClinicalMedicine · 2024Article
- Review
- The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- Towards a robust and compact deep learning system for primary detection of early Barrett's neoplasia: Initial image-based results of training on a multi-center retrospectively collected data set.United European gastroenterology journal · 2023Article
- Genetically Optimized Modular Neural Networks for Precision Lung Cancer Diagnosis: Exploratory Study of Novel Approach.Cancer diagnosis & prognosisArticle
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Authors and funding
24 authors at 15 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEndoscopic detection of early neoplasia in Barrett's esophagus is difficult. Computer Aided Detection (CADe) systems may assist in neoplasia detection. The aim of this study was to report the first steps in the development of a CADe system for Barrett's neoplasia and to evaluate its performance when compared with endoscopists.
methodsThis CADe system was developed by a consortium, consisting of the Amsterdam University Medical Center, Eindhoven University of Technology, and 15 international hospitals. After pretraining, the system was trained and validated using 1.713 neoplastic (564 patients) and 2.707 non-dysplastic Barrett's esophagus (NDBE; 665 patients) images. Neoplastic lesions were delineated by 14 experts. The performance of the CADe system was tested on three independent test sets. Test set 1 (50 neoplastic and 150 NDBE images) contained subtle neoplastic lesions representing challenging cases and was benchmarked by 52 general endoscopists. Test set 2 (50 neoplastic and 50 NDBE images) contained a heterogeneous case-mix of neoplastic lesions, representing distribution in clinical practice. Test set 3 (50 neoplastic and 150 NDBE images) contained prospectively collected imagery. The main outcome was correct classification of the images in terms of sensitivity.
resultsThe sensitivity of the CADe system on test set 1 was 84%. For general endoscopists, sensitivity was 63%, corresponding to a neoplasia miss-rate of one-third of neoplastic lesions and a potential relative increase in neoplasia detection of 33% for CADe-assisted detection. The sensitivity of the CADe system on test sets 2 and 3 was 100% and 88%, respectively. The specificity of the CADe system varied for the three test sets between 64% and 66%.
conclusionThis study describes the first steps towards the establishment of an unprecedented data infrastructure for using machine learning to improve the endoscopic detection of Barrett's neoplasia. The CADe system detected neoplasia reliably and outperformed a large group of endoscopists in terms of sensitivity.
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