Optimisation and clinical validation of a digital diagnostic system for small bowel biopsies

Question: This proposal addresses a significant unmet need, for accurate and timely duodenal biopsy diagnosis.

Background: The international shortage of histopathologists and lack of automation in histopathology cause delays in biopsy diagnosis and backlogs, with knock-on effects for patient care.

There is disagreement between histopathologists about the diagnosis of 20% of duodenal biopsies, particularly in diagnosing coeliac disease.

While 1% of the population has this diagnosis, 2% remains undiagnosed, with symptoms including abdominal pain, diarrhoea, vomiting, malaise, fatigue, mouth ulcers and itchy skin rashes.

Longer term complications include duodenal adenocarcinoma, lymphoma, vitamin deficiency, anaemia, osteoporosis and infertility.

Innovate UK-funded infrastructure introduced high throughput scanning of microscope slides, which histopathologists can view on a screen, rather than under a microscope. The scanned images provide the opportunity to automate biopsy diagnosis.

Lyzeum has developed software, with >96% accuracy, that uses multiple instance learning, breaking the large scanned images into small “tiles”, and predicts likely diagnosis of each tile, to classify biopsy images based on the percentages of tiles with particular diagnostic labels.

Aims: 1.We will optimise and validate our software solution for the fully automated reading of duodenal biopsies, dividing them into “normal” (≈73%), coeliac disease (≈12%) and “for histopathologist review” (