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DETECTION OF CEREBRAL ISCHEMIA BASED ON MACHINE LEARNING

Project description

A closer look at stroke diagnosis

A computed tomography (CT) scan is one of the most effective imaging modalities for diagnosing a stroke, which is a leading cause of mortality, morbidity and severe long-term disability. As such, CT perfusion (CTP) technology can play an important role in the detection and characterisation of stroke. But it is not without limitations. For instance, CTP is not supported by AI analysis. The EU-funded DECIMAL project will research and develop novel algorithms to natively analyse CTP data and provide quick treatment advice. Specifically, the project will recruit an innovation associate to explore the technical and practical feasibility of developing data-driven CTP algorithms.

Objective

Stroke is the second cause of morbidity and the leading cause of long-term disability. More than 1.1 million people in Europe suffer a stroke each year, which will increase to 1.5 million in 2025 due to an ageing population and unhealthy lifestyle. Stroke diagnosis and care is notoriously complicated. Some improvements have been made in the clinic, such as through the introduction of CT Perfusion imaging technology (CTP) to allow for quantitation. There are, however, many concerns with current implementations resulting in poor accuracy.

Nico.lab develops and markets unique Artificial Intelligence (AI) technology which analyzes brain imagery – such as a CT or MRI scan – and provides health professionals with treatment advice. CTP is a vastly different technology and is thus not yet supported by our AI analysis. Therefore, we want to research and develop novel algorithms to natively analyze CTP data and provide quick treatment advice.

As we have no experience at all with CTP technology – not medically nor concerning software, and barely in a research capacity - we require someone who holds all these expertises. There are, however, several barriers currently withholding us, ranging from our lacking resources to demontstrably unavailable talent in The Netherlands.

With this Innovation Associate grant we wish to hire the right talent. In this project the innovation associate will explore the technical and practical feasibility of developing data driven CTP algorithms. The innovation associate will obtain technical, practical and soft skills. Finally, the associate will deliver an innovation programme roadmap which can be implemented after finalization of this project.

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Programme(s)

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Topic(s)

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Funding Scheme

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CSA - Coordination and support action

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Call for proposal

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(opens in new window) H2020-INNOSUP-2018-2020

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Coordinator

NICO-LAB BV
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 137 747,50
Address
PAASHEUVELWEG 25
1105 BP Amsterdam
Netherlands

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SME

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

Yes
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
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Total cost

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