Scientists Utilize AI To More Easily Diagnose Celiac Disease
Cambridge scientists have developed a machine learning algorithm using artificial intelligence (AI) to help determine if an individual has celiac disease, which was previously a long-term process to get a proper diagnosis.
The algorithm was able to correctly identify the presence of the disease in 97 out of 100 biopsies from a pool of 3,400 scanned samples from four NHS hospitals, according to reports. This is a major development for pathologists, as the tool can be used to not only speed up results, but improve overall techniques for other potential diseases as well.
Pathologists are also in high demand, so having a tool that can help take the pressure off of their intense workload will make identifying disease more accessible for patients. The basis of this research comes from studies focused on detecting cancer in the body, but scientists realized the tools can be used for other diseases as well.
Scientists at the University of Cambridge decided to utilize the tools to focus on celiac disease, an autoimmune disease that is triggered by the consumption of gluten. Symptoms of the disease vary widely between individuals, making it difficult for many patients to receive an accurate diagnosis.
In order to diagnose celiacs, pathologists need to look at a biopsy from part of the small intestine under a microscope. Interpreting these biopsies, however, can be subjective, hence how difficult it is to diagnose.
The researchers for Cambridge developed the machine learning algorithm in order to classify biopsy image data. Senior author Professor Elizabeth Soilleux from the Department of Pathology and Churchill College, University of Cambridge, said:
“Celiac disease affects as many as one in 100 people and can cause serious illness, but getting a diagnosis is not straightforward.”
“It can take many years to receive an accurate diagnosis, and at a time of intense pressures on health care systems, these delays are likely to continue. 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,” Professor Soilleuz stated.
The team tested the algorithm using 650 images from an independent data set, and found that the model was correct in its diagnosis in over 97 out of 100 cases.
The group also emphasized that this is a great step in advancement for the world of pathology, because previous data sets have shown that pathologists can, and have, disagree on a diagnosis. Dr. Florian Jaeckle, from the Department of Pathology, and a Research Fellow at Hughes Hall, Cambridge, said, “This is the first time AI has been shown to diagnose as accurately as an experienced pathologist whether an individual has celiac or not.
“Because we trained it on data sets generated under a number of different conditions, we know that it should be able to work in a wide range of settings, where biopsies are processed and imaged differently.
“This is an important step towards speeding up diagnoses and freeing up pathologists’ time to focus on more complex or urgent cases. Our next step is to test the algorithm in a much larger clinical sample, putting us in a position to share this device with the regulator, bringing us nearer to this tool being used in the NHS.”
“When we speak to patients, they are generally very receptive to the use of AI for diagnosing celiac disease,” Dr. Jaeckle said.
“This no doubt partly reflects their experiences of the difficulties and delays in receiving a diagnosis.
One issue that comes up frequently with both patients and clinicians is the issue of ‘explainability’—being able to understand and explain how AI reaches its diagnosis. It’s important for us as researchers and for regulators to bear this in mind if we want to ensure there is public trust in applications of AI in medicine.”
“During the diagnostic process, it’s vital that patients keep gluten in their diet to ensure that the diagnosis is accurate. But this can cause uncomfortable symptoms. That’s why it’s really important that they are able to receive an accurate diagnosis as quickly as possible,” said Keira Shepherd, Research Officer at Celiac UK.
“This research demonstrates one potential way to speed up part of the diagnosis journey … we hope that one day this technology will be used to help patients receive a quick and accurate diagnosis.”
Eric Mastrota is a Contributing Editor at The National Digest based in New York. A graduate of SUNY New Paltz, he reports on world news, culture, and lifestyle. You can reach him at eric.mastrota@thenationaldigest.com.
