Objective
Worldwide due to population ageing there will be 75 million patients with Alzheimer's disease by 2030. Alzheimer's is a disease described by accelerated brain ageing which leads to decline in both memory and cognitive skills, thus affecting everyday activities of those suffering. On average over 60% of patients do not receive a diagnosis and average cost of misdiagnosis is estimated to be over 2K €. Existing screening and early diagnostics methods lack speed and accuracy, are not automated enough, and detect only already existing symptoms when it is late for prevention.
At Neurosalience we develop a software for assessing brain ageing for early detection of Alzheimer's disease from low-resolution MRI and CT scans. Measuring subject’s brain age and extracting important features for making a prediction are used to assess the risk of Alzheimer's disease. The tool determines whether patient has Alzheimer's along with its stage and type of dementia. We aim at supplying healthcare providers with a more accurate, cheaper and faster early diagnostic tool for Alzheimer's disease. The tool allows decreasing patient-related costs and improving the quality of life of patients. The tool is first in the world to be capable of processing even low-resolution MRI data and providing interpretable results over whole brain.
With the support of the Women TechEU programme we will boost the market readiness of the Neurosalience tool by developing the project both from business and regulatory perspectives. For this purpose, we plan to develop a pricing model for the tool, improve deep learning models used in the product and prepare all the documentation along with quality management system required for regulatory approval. We would benefit from the Women TechEU services of mentoring and coaching on how to support and run investor relationships. As we plan to set up partnerships with hospitals, we would also benefit from advice on running such partnerships in the healthcare.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencessoftware
- medical and health sciencesbasic medicineneurologydementiaalzheimer
- engineering and technologymedical engineeringdiagnostic imagingcomputed tomography
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
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Programme(s)
- HORIZON.3.2 - European innovation ecosystems Main Programme
Funding Scheme
HORIZON-CSA - HORIZON Coordination and Support ActionsCoordinator
10313 Tallinn
Estonia
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.