Descrizione del progetto
Computer per diagnosticare come i medici umani
Il potenziale dell’intelligenza artificiale nell’assistenza sanitaria sta crescendo. Quando si tratta di fare diagnosi mediche, i computer potrebbero avere lo stesso successo dei medici umani. Il progetto MrDoc, finanziato dall’UE, ha sviluppato una piattaforma di apprendimento artificiale semi-supervisionata in grado di analizzare e interpretare set di dati medici. Ha progettato un processo che imita l’immaginazione umana creativa per rilevare e diagnosticare rapidamente alcune malattie non trasmissibili quali le malattie cardiovascolari e il diabete, utilizzando parametri biometrici (pressione arteriosa, variabilità della frequenza cardiaca, emoglobina, glicemia) con un alto livello di precisione. Il progetto si sta preparando per il mercato, per vendere e concedere in licenza la sua soluzione a tre gruppi target: pazienti, sviluppatori di strumenti software e hardware (nonché App) e aziende farmaceutiche.
Obiettivo
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.
Campo scientifico
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
- medical and health sciencesclinical medicinecardiologycardiovascular diseasescardiac arrhythmia
- natural sciencescomputer and information sciencesartificial intelligencemachine learningsemisupervised learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- medical and health sciencesclinical medicineendocrinologydiabetes
- social scienceseducational sciencespedagogyactive learning
Programma(i)
Argomento(i)
Invito a presentare proposte
Vedi altri progetti per questo bandoBando secondario
H2020-SMEInst-2018-2020-1
Meccanismo di finanziamento
SME-1 - SME instrument phase 1Coordinatore
00146 ROMA
Italia
L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.