Dieta’s Stool Image AI is Validated as Superior to Gastroenterology’s Industry Standard, Published Clinical Study

Dieta Health, a gastroenterology digital health company, announced the peer-reviewed publication of a clinical study that found its AI to be comparable in accuracy to expert gastroenterologists and superior to patients in classifying stool.

Dieta is revolutionizing digestive healthcare with its personalized IBS care platform. The company has built the world’s first mobile app with stool image recognition technology, making the measurement of digestive outcomes (poop) objective instead of subjective. The technology was validated in a published clinical trial at Cedars-Sinai, proving to be more accurate than the current industry standard of patient reported outcomes.

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CEO and Founder, Asaf Kraus, is a Data Scientist and IBS patient, passionate about solving digestive health issues for millions of others who have suffered like him. In 2017, at the height of severity of his IBS, Kraus spent all his time outside of work consulting with gastroenterologists and other clinicians and experimenting with dozens of treatment options. Kraus learned a great deal about the painful and confusing journey of an IBS patient. Given his machine learning expertise as a Data Scientist at Uber, he formed a belief that data and AI can help solve some of these problems.

The most significant revelation came years ago when Kraus participated as a subject in a clinical trial, testing an experimental medication. During that experience, he learned how the scientific community was measuring results of interventions on stool – by asking patients to remember and subjectively classify their stool on the Bristol Scale.

As a result of this experience matched with data science expertise, Dieta built a mobile app for iOS and Android which lets patients conveniently snap an image of each bowel movement, and instantly classifies objective and accurate digestive health results. In addition to the Bristol Scale, the Dieta app also classifies multiple other stool characteristics including volume and fragmentation from a single image capture.

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