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Development and commercialization of a semi-supervised learning AI for robust diagnosis in real world settings.

Project description

Computers to diagnose like human doctors

The potential for artificial intelligence in healthcare is growing. When it comes to making medical diagnoses, computers may be just as successful as human physicians. The EU-funded MrDoc project has developed a semi-supervised learning AI platform that can analyse and interpret medical datasets. It has designed a process that mimics creative human imagination to quickly detect and diagnose some non-communicable diseases such as cardiovascular disease and diabetes, using biometric parameters (blood pressure, heart rate variability, haemoglobin, blood glucose) with a high level of accuracy. The project is preparing for market, to sell and licence its solution to three target groups: patients, developers of software and hardware tools (as well as Apps) and pharmaceutical companies.


Non-communicable diseases such as cardiovascular diseases, diabetes, are by far the leading cause of death in the world and a growing burden for patients, healthcare providers and local economies. Despite many NCDs conditions like cardiac arrhythmia, diabetes, hypertension can be cured with early detection, they don’t often show symptoms. During their medical check-up, medical practitioners (GP) can’t be accurate as specific examinations (e.g. EGCs, blood tests), resulting in a growing number of errors or false negative/positive, which represent for Healthcare systems and additional financial burden. People are usually discouraged from doing specific examination due to long waiting time, invasiveness of medical tests and additional costs.Even if technological advancements have led to AI based easy-to-use solutions able to contribute positively to easy and early detection of diseases and pre-diseases condition, they come along with many significant limitations, such as the need to train on huge amounts of labelled data and difficulties in managing inputs that are noisy, incomplete or simply different from the original dataset (such data generated from a smartphone camera).This results in limited accuracy or significant costs and time consume for labelling of data. We have developed a platform based on a semi-supervised learning AI, able to analyse and interpret medical dataset through a process that mimics human creative imagination and, in a very short timeframe, detect and diagnose some NCDs and biometric parameters (blood pressure, Heart rate variability, haemoglobin, blood glucose) from “dirty” signals, generated by consumer electronics devices (smartphones, closed circuit cameras, etc.), with a high level of accuracy overcoming existing limitations.We aim at selling and licence our solution to 3 main targets: - final consumers/patients, - producers/owners of software and hardware tools (as well as Apps) in Health sector, Pharmaceutical companies.

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Net EU contribution
€ 50 000,00
00146 ROMA

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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Centro (IT) Lazio Roma
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Total cost
€ 71 429,00