Neurocloud | CLOUD-BASED MULTIMODAL NEUROIMAGING PLATFORM

Summary
One of the main socioeconomics challenges to be faced in the next decades will be the prevalence of neurodegenerative diseases (such as Alzheimer’s and Parkinson’s) due to population ageing. Today, diagnosis, treatment and monitoring of such illness is complex, a laborious process completed by a limited set of Neurologist experts who need to manually process and compare images from multiple Brain medical imaging systems (MRI, PET and SPECT). While early detection is the key to reduce costs and impact of the diseases, they are routinely under-diagnosed by up to 60-80% in the very early stages. One of the main weaknesses of the diagnostic process is the physician’s visual assessment due to the current subjective interpretation of complex images.

Qubiotech was created in 2014, with a mission to radically speed-up and improve brain diseases diagnostic work flow. Spun off from the University Hospital of Santiago de Compostela and based on a novel algorithm for fast composite multimodal and quantitative analysis of medical images. We have a product that can forge a new way to do things. Rapidly increasing diagnosis speeds whilst lowering costs, our aim is to overhaul patient treatment in this critical sector.

We now want to finalise the feasibility assessment for our entire product roadmap whilst confirming that we have everything in place to achieve the detected opportunity. Aside from helping to alleviate the incoming ageing societal challenge, we aim to plant our company as the European leader of a new sector and drive revenues of 15.5M€ by 2022.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/816437
Start date: 01-06-2018
End date: 30-11-2018
Total budget - Public funding: 71 429,00 Euro - 50 000,00 Euro
Cordis data

Original description

One of the main socioeconomics challenges to be faced in the next decades will be the prevalence of neurodegenerative diseases (such as Alzheimer’s and Parkinson’s) due to population ageing. Today, diagnosis, treatment and monitoring of such illness is complex, a laborious process completed by a limited set of Neurologist experts who need to manually process and compare images from multiple Brain medical imaging systems (MRI, PET and SPECT). While early detection is the key to reduce costs and impact of the diseases, they are routinely under-diagnosed by up to 60-80% in the very early stages. One of the main weaknesses of the diagnostic process is the physician’s visual assessment due to the current subjective interpretation of complex images.

Qubiotech was created in 2014, with a mission to radically speed-up and improve brain diseases diagnostic work flow. Spun off from the University Hospital of Santiago de Compostela and based on a novel algorithm for fast composite multimodal and quantitative analysis of medical images. We have a product that can forge a new way to do things. Rapidly increasing diagnosis speeds whilst lowering costs, our aim is to overhaul patient treatment in this critical sector.

We now want to finalise the feasibility assessment for our entire product roadmap whilst confirming that we have everything in place to achieve the detected opportunity. Aside from helping to alleviate the incoming ageing societal challenge, we aim to plant our company as the European leader of a new sector and drive revenues of 15.5M€ by 2022.

Status

CLOSED

Call topic

EIC-SMEInst-2018-2020

Update Date

27-10-2022
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Horizon 2020
H2020-EU.2. INDUSTRIAL LEADERSHIP
H2020-EU.2.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
H2020-EU.2.1.0. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Cross-cutting calls
H2020-EIC-SMEInst-2018-2020
H2020-SMEInst-2018-2020-1
H2020-EU.2.3. INDUSTRIAL LEADERSHIP - Innovation In SMEs
H2020-EU.2.3.0. INDUSTRIAL LEADERSHIP - Innovation In SMEs - Cross-cutting calls
H2020-EIC-SMEInst-2018-2020
H2020-SMEInst-2018-2020-1
H2020-EU.3. SOCIETAL CHALLENGES
H2020-EU.3.0. Cross-cutting call topics
H2020-EIC-SMEInst-2018-2020
H2020-SMEInst-2018-2020-1