Title: Development of an artificial intelligence solution for diagnosis and assessment of severity of pathology in small intestinal biopsies in suspected or known coeliac disease.
Funder: Coeliac UK

Abstract: The UK prevalence of coeliac disease is increasing, with a suggestion that only 1/9 cases are detected. Average time from symptoms to diagnosis is 13 years. Assessing whether a patient has coeliac disease (CD), or features of gluten exposure in a clinical trial, relies primarily on “gold standard” small intestinal biopsy examination by a pathologist; an unavoidably subjective process with poor inter-observer concordance (in up to 25% cases), variable concordance with serology and a high rate of “equivocal” biopsies. There is a clear unmet need for a more sensitive, objective and reproducible testing strategy in the diagnosis of gluten-sensitivity. We have assembled a multi-disciplinary team to undertake this timely project; building on contemporary progress in neural networks. The Soilleux group has a carefully clinicopathologically annotated, high-resolution cohort of digitised images of 3-4 small intestinal biopsy samples per patient from 500 patients, which in collaboration with the Schönlieb group and the University of Edinburgh, will be used to develop components of an artificial intelligence image analysis solution for CD diagnosis. Supported by a successful life-sciences entrepreneur, we have founded a spin-out company “Lyzeum” as a vehicle for its commercialisation as a digital pathology platform. During year 1, two Lyzeum-funded postdoctoral researchers with mathematics, physics/ engineering/ computer science backgrounds will work together on these image datasets; one constructing a method of segmenting images into different components/ areas and considering the challenges of analysing images produced with multiple different scanner types, while the second develops a bespoke convolutional neural network for CD diagnosis. At the end of year 1, we will combine these two approaches to deliver a small-intestinal biopsy image classification solution. We will submit at least one conference abstract at this time and protect intellectual property as relevant. During year 1, the academic/ NHS collaborators will produce additional relevant clinical datasets to support algorithm development in year 2. During year 2, an additional 700 image set, including CD, CD on gluten-free diet, normal and CD mimics, will be used for further algorithm training, to test diagnostic accuracy in CD and to further investigate the potential for identifying biological features or similarities in biopsies previously overlooked by classical approaches. We anticipate completing validation of a fully automated solution (figure 1) in 2 years following this grant period, by running the algorithm as a cloud-based, pathologist’s diagnostic support system. Additionally, this approach will assist classification of biopsies in the spectrum between classic coeliac disease and normal and may permit accurate delineation of novel subtypes of coeliac disease.

Lay abstract: The current gold standard for the diagnosis of coeliac disease (CD) is inspection by a trained pathologist of an endoscopic biopsy of the small intestine in order to identify damage to intestinal cells that are characteristic of the condition. This process is inherently time-consuming and subjective; with different opinions in up to 25% of cases. Moreover, results are filed individually with no opportunity to learn from patterns or similarities in the biopsies or the patients from which they are obtained. Digital pathology, where biopsy slides are scanned and stored centrally at high resolution, in combination with recent advances in image processing, has the capability to transform this situation. Generic algorithms, which Google might use to identify dogs or diamonds in online images, should be able to classify the majority of biopsies as either normal or diseased while specific algorithms, engineered to include an understanding of the underlying biology, have the potential to further improve this diagnosis while also providing a framework in which similar biopsy images might be identified and used to highlight potential disease subtypes or the presence of other related conditions. This grant will enable a multi-disciplinary team of mathematicians and pathologists from the Universities of Cambridge and Edinburgh to validate these approaches on an existing database of 1500 biopsy images. In year 1 mathematicians from Cambridge will combine generic machine learning approaches with novel methods developed specifically for this application in order to develop a computer programme that can diagnose biopsies as either diseased or normal. In year 2 these approaches will be extended and implemented in a centralised, cloud based, digital pathology solution that can be made available to clinicians and researchers, as a tool to help with diagnosis. Biopsy images uploaded to the cloud will help with validation of the computer programme as a fully automated system. A traffic light system will be used to highlight ambiguous cases, where an expert opinion is still required, while a classification system will be used to bring together similar biopsies and highlight recurrent features as a basis for further research. The grant will be supported by Lyzeum; an investor backed spin-out from the University of Cambridge, created in order to continue the development and commercialisation of this software, which, once fully validated, will be made available internationally as a platform able to automate the analysis of most, if not all, of coeliac biopsies; freeing up valuable pathologists and potentially identifying a range of associated disorders and new disease subtypes.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: ORAL AND GASTROINTESTINAL;INFLAMMATORY AND IMMUNE SYSTEM