A machine learning algorithm developed by Cambridge scientists was able to correctly identify in 97 cases out of 100 whether or not an individual had coeliac disease based on their biopsy, new research has shown. The AI tool, which has been trained on almost 3,400 scanned biopsies from four NHS hospitals, could speed up diagnosis of the condition and take pressure off stretched healthcare resources, as well as improving diagnosis in developing nations, where shortages of pathologists are severe.

Digital tools that can speed up or even automate analysis of diagnostic tests are beginning to show real promise for reducing the demands on pathologists. A large amount of this work has focused on the detection of cancer, but researchers are beginning to look at opportunities to diagnose other types of disease. One condition being looked at by scientists at the University of Cambridge is coeliac disease, an autoimmune disease trigged by consuming gluten. It causes symptoms that include stomach cramps, diarrhoea, skin rashes, weight loss, fatigue and anaemia.

Because symptoms vary so much between individuals, patients often have difficulty in receiving an accurate diagnosis. The gold standard for diagnosing coeliac disease is via a biopsy of the duodenum (part of the small intestine). Pathologists will then analyse the sample under a microscope or on a computer to look for damage to the villi, tiny hair-like projections that line the inside of the small intestine.

Interpreting biopsies, which often have subtle changes, can be subjective. Pathologists use a classification system known as the Marsh-Oberhuber scale to judge the severity of a case, ranging from zero (the villi are normal and the patient is unlikely to have the disease) to four (the villi are completely flattened). AI has the potential to speed up this process, allowing patients to receive a diagnosis faster, while at the same time taking pressure off NHS waiting lists.

Source: https://www.cam.ac.uk/stories/AI-and-coeliac-disease