HemoTech AI
By 2030, there will be five times more diabetics in the world. Standard methods for diabetes diagnosis are invasive, expensive, and painful, with a high margin of error in measurements. We have created an innovative non-invasive glucometer based on Raman spectroscopy and neural networks with an accuracy of 5%.
Biotechnology And National Health
Technologies for human health
3 years
Russia, CIS countries, MENA, China, India
The advantage over existing technologies lies in the absence of consumables, low cost, speed (30 seconds), and high accuracy of the device.
Grant recipient "Student Startup", winner of the first season of the Innovators Academy accelerator of Innovation Agency of Moscow, winner of the TOP-50 university startups, winner of the SechenovTech accelerator, participant of the Open Pre-Accelerator of the Skolkovo Technopark, winner of the Skolkovo Management School Startup Academy, winner of the International Artificial Intelligence Award "Gravitation," Investment Award "Summit," and the All-Russian Competition for Young Entrepreneurs Unovus.
Development of a portable spectrophotometer using artificial neural networks for non-invasive determination of glycated hemoglobin in blood by Raman spectroscopy, Using neural networks for non-invasive determination of glycated hemoglobin levels, illustrated by the application of an innovative portable glucometer in clinical practice
