Machine Learning Achieves Pathologist-Level Celiac Disease Diagnosis

Abstract

Our model achieved pathologist-level performance in diagnosing the presence or absence of coeliac disease from a representative set of duodenal biopsies, representing a significant advancement towards the adoption of machine learning in clinical practice. Additionally, it demonstrated strong generalisability, performing equally well on biopsies from a previously unseen hospital. We concluded that our model has the potential to revolutionise duodenal biopsy diagnosis by accurately identifying or ruling out coeliac disease, thereby significantly reducing the time required for pathologists to make a diagnosis.