Researchers Develop Non Invasive Sticker Patch For Faster Di
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A sticker patch devised by Technion scientists can catch compounds released by the skin and is a novel means of diagnosing tuberculosis. The analysis of these compounds by using artificial intelligence (AI) can provide a quick, non invasive diagnosis. In future implementations, the group plans to integrate the sensors into the patch and use a smartphone to read its results.

Early symptoms of tuberculosis are non-specific, complicating diagnosis. What makes matters worse is that currently existing diagnosis methods are slow, and at times too expensive or complex for resource-limited settings. For example, a sputum smear is too expensive in a location where people live on $1/day, while a mycobacterial culture test takes 4–8 weeks and at least three visits by the patient to finalize the diagnosis and begin treatment.

WHO regards a fast, cheap, and efficient tuberculosis test as crucial to fighting the disease. And it is this need that the team of Professor Hossam Haick from the Wolfson Department of Chemical Engineering at the Technion address in their ground-breaking study. Led by Dr. Rotem Vishinkin, the group created a sticker patch to be applied on the patient's arm.

Containing a pouch of absorbing material, the patch collected compounds released through the skin. These provided the sought-after diagnostic tool. A device based on this proof-of-concept study, called A-patch, is already undergoing clinical trials. Dr. Vishinkin, the project's scientific leader, explained, our initial studies, done on a large number of subjects in India and in South Africa showed high effectiveness in diagnosing tuberculosis, with over 90% sensitivity and over 70% specificity.

They showed that tuberculosis can be diagnosed through the compounds released by the skin. Their current challenge is minimizing the size of the sensor array and fitting it into the sticker patch. The platform the group is developing is cheap, fast, and simple in its utilization, and requires no specially trained personnel.

The group hopes the same methodology and the same platform could in the future be used to diagnose other diseases and conditions, making effective diagnosis accessible to remote areas in the world.

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