🧠 The BONS-AI Consortium

Partner institutions of the BONS-AI Consortium.

🌍 About Us

The BONS-AI Consortium is an international research collaboration focused on advancing artificial intelligence in gastrointestinal endoscopy.

The consortium consists of 15 tertiary referral centers specializing in the management of early Barrett’s neoplasia, coordinated by:

• Amsterdam University Medical Center (AUMC)
• Eindhoven University of Technology (TU/e)

📚 Learn More

Highlighted publications:

• GastroNet-5M: A Multicenter Dataset for Developing Foundation Models in Gastrointestinal Endoscopy
  Gastroenterology (2025) – https://doi.org/10.1053/j.gastro.2025.07.030

• Foundation Models in Gastrointestinal Endoscopic AI: Impact of Architecture, Pre-training Approach and Data Efficiency
  Medical Image Analysis (2024) – https://doi.org/10.1016/j.media.2024.103298

• A deep learning system for detection of early Barrett's neoplasia: a model development and validation study
  The Lancet Digital Health (2023) – https://doi.org/10.1016/S2589-7500(23)00199-1

• Deep-learning system detects neoplasia in patients with Barrett’s esophagus with high accuracy
  Gastroenterology (2019) – https://doi.org/10.1053/j.gastro.2019.11.030

Latest publications:

• Evaluation of an improved computer-aided detection system for Barrett’s neoplasia in real-world imaging conditions
  Endoscopy (2025) – https://doi.org/10.1055/a-2642-7584

• The development and ex vivo evaluation of a computer-aided quality control system for Barrett’s esophagus endoscopy
  Endoscopy (2025) – https://doi.org/10.1055/a-2537-3510

• Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems
  Endoscopy (2025) – https://doi.org/10.1055/a-2530-1845

• Challenges in Implementing Endoscopic Artificial Intelligence: The Impact of Real-World Imaging Conditions on Barrett’s Neoplasia Detection
  United European Gastroenterology Journal (2025) – https://doi.org/10.1002/ueg2.12760

• Will Transformers change gastrointestinal endoscopic image analysis? A comparative analysis between CNNs and Transformers, in terms of performance, robustness and generalization
  Medical Image Analysis (2025) – https://doi.org/10.1016/j.media.2024.103348

• Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies
  Medical Image Analysis (2024) – https://doi.org/10.1016/j.media.2024.103157

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