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

Projektbeschreibung

Computer für Diagnosen wie von menschlichen Ärztinnen und Ärzten

Das Potenzial für künstliche Intelligenz im Gesundheitsbereich wächst. Wenn es darum geht, medizinische Diagnosen zu stellen, sind Computer möglicherweise genauso erfolgreich wie menschliche Ärztinnen und Ärzte. Das EU-finanzierte Projekt MrDoc hat eine semi-überwachte, lernende KI-Plattform entwickelt, die medizinische Datensätze analysieren und interpretieren kann. Es hat einen Prozess konzipiert, der das kreative menschliche Vorstellungsvermögen nachahmt, um einige nicht übertragbare Krankheiten wie Herz-Kreislauf-Erkrankungen und Diabetes schnell zu erkennen und zu diagnostizieren, wobei biometrische Parameter (Blutdruck, Herzfrequenzvariabilität, Hämoglobin, Blutzucker) mit einem hohen Grad an Genauigkeit ausgewertet werden. Das Projekt bereitet die Markteinführung vor, um seine Lösung an drei Zielgruppen zu verkaufen und zu lizenzieren: Patientinnen und Patienten, Entwicklerinnen und Entwickler von Software- und Hardware-Instrumenten (sowie Apps) und Pharmaunternehmen.

Ziel

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.

Wissenschaftliches Gebiet

Programm/Programme

Thema/Themen

EIC-SMEInst-2018-2020 - SME instrument

Aufforderung zur Vorschlagseinreichung

H2020-EIC-SMEInst-2018-2020

Andere Projekte für diesen Aufruf anzeigen

Unterauftrag

H2020-SMEInst-2018-2020-1

Finanzierungsplan

SME-1 - SME instrument phase 1

Koordinator

MR DOC SRL

Netto-EU-Beitrag

€ 50 000,00

Adresse

VIA PIETRO BLASERNA 40
00146 ROMA
Italien

KMU

Ja

Region

Centro (IT) Lazio Roma

Aktivitätstyp

Private for-profit entities (excluding Higher or Secondary Education Establishments)

Links

Die Organisation kontaktieren

Teilnahme an EU-FuI-Programmen

HORIZON-Kooperationsnetzwerk

Gesamtkosten

€ 71 429,00