Esophageal adenocarcinoma (EAC) is frequently diagnosed at a late stage, which leads to unfavorable patient outcomes. Barrett’s esophagus (BE) is currently the only recognized precursor lesion for EAC. Accurate pathological diagnosis is critical for optimal patient management. However, histopathological assessment is often subjective, resulting in considerable variability in patient treatment outcomes.

This project aims to develop more objective diagnostic tools utilizing AI to enhance the management of patients with BE. We aim to develop an AI system that can relate histopathological imaging data and clinical data to disease progression, allowing for the identification of new biomarkers to aid clinical decision-making. Ultimately, this will promote a more personalized approach to patient care, reduce the burden on healthcare providers and the healthcare system budget, and enhance the quality of life of patients with Barrett’s esophagus.

This project is a collaboration between the qurAI group, the pathology department of the Amsterdam UMC and the Amsterdam Machine Learning Lab (AMLab) and is funded by the Maag-Lever-Darm-Stichting.