Tongue health has been a key indicator of illnesses for millennia. Credit: Deposit Photos A new artificial intelligence machine learning model is capable of accurately diagnosing certain illnesses nearly every time by simply looking at a patient’s tongue . The novel technology, while state-of-the-art, draws its inspiration from medical approaches utilized by humans for over 2,000 years.
When it comes to diagnosing ailments, traditional Chinese medicine and other practices often turn to the tongue for clues. Based on its color, shape, and thickness, the muscle can reveal a number of possible health issues—from cancer, to diabetes, to even asthma and gastrointestinal issues. Now, after more than two millennia of peering into patient mouths for answers, doctors may soon receive a second opinion from artificial eyes powered by machine learning.
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“Human tongues possess unique characteristics and features connected to the body’s internal organs, which effectively detect illnesses and monitor their progress… Among these, tongue color is of the most importance,” a team of engineering researchers collaborating between the University of South Australia (UniSA) and Iraq’s Middle Technical University (MTU) in a recent study published in the journal, Technologies .
Ali Al-Naji, the paper’s senior author and an adjunct associate professor in UniSA’s Department of Medical Instrumentation Techniques Engineering, offered a number of scenarios in the study’s August 13 announcement .
“Typically, people with diabetes have a yellow tongue; cancer patients a purple tongue with a thick greasy coating; and acute stroke patients present with an unusually shaped red tongue,” he explained. A white tongue, meanwhile, can be indicative of anemia, while indigo or violet color points to vascular and gastrointestinal issues or asthma. In more recent cases, deep red tongues may provide evidence of severe COVID-19 cases. A researcher demonstrates how a camera captures images of the tongue and analyzes it for disease. Credit: Middle Technical University Like similar visual machine learning algorithmic programs, Al-Naji’s team constructed their own system by visually training it on two data sets. First, they fed 5,260 images spanning seven colors across different saturations and light […]
AI tongue scanner can diagnose illnesses with 96 percent accuracy
