DURHAM, N.C. -- An artificial intelligence tool being developed by Duke scientists can be added to the standard toilet to help analyze patients' stool and give gastroenterologists the information they need to provide appropriate treatment for chronic issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS).
The work is being done by Duke University's Center for Water, Sanitation, Hygiene and Infectious Disease (WaSH-AID), and was presented Saturday at the virtual conference Digestive Disease Week 2021.
"Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, associate professor of medicine at Duke University and one of the lead authors on the study.
"Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process," Fisher said. "The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."
For example, a flare of IBD could be diagnosed using Smart Toilet and response to a drug or diet treatment could be monitored. If the technology were installed in bathrooms in a long term care facility, initial diagnosis of acute conditions could be improved.
The technology can be retrofitted within the pipes of an existing toilet. Once a person has a bowel movement and flushes, the toilet will take an image of the stool within the pipes. The data collected over time will provide a gastroenterologist a better understanding of a patient's stool form (i.e., loose, normal or constipated) and the presence of blood, allowing them to diagnose the patient and provide the right treatment for their condition.
To develop the artificial intelligence image analysis tool for the Smart Toilet, researchers analyzed 3,328 unique stool images found online or provided by research participants. All images were reviewed and annotated by gastroenterologists according to the Bristol Stool Scale, a common clinical tool for classifying stool. Using a convolutional neural network, a type of deep learning algorithm that can analyze images, researchers found that the algorithm accurately classified the stool form 85 percent of the time and gross blood detection was accurate in 76 percent of the images.
"We are optimistic about patient willingness to use this technology because it's something that can be installed in their toilet's pipes and doesn't require the patient to do anything other than flush," said Sonia Grego, PhD, founding director of the Duke Smart Toilet Lab and a lead researcher on the study. "This could be especially useful for patients who may not be able to report their conditions, such as those who live in a long-term care facility."
The prototype has promising feasibility, but it is not yet available to the public. Researchers are developing additional features of the technology to include stool specimen sampling for biochemical marker analysis that will provide highly specific disease data to meet the needs of patients and gastroenterologists.
CITATION: "Automated Stool Image Analysis by Artificial Intelligence in a Smart Toilet," Deborah Fisher, MD. Digestive Disease Week, Saturday, May 22, 2021, abstract Sa652. www.ddw.org/press.
