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DECIMAL

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DETECTION OF CEREBRAL ISCHEMIA BASED ON MACHINE LEARNING

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 un... ver más

30/09/2021

Nicolab

138K€

Presupuesto del proyecto: 138K€

Líder del proyecto

NICOLAB BV No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-09-30

INNOSUP-02-2019-2020: European SME innovation Associate -...

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Cerrada hace 6 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

Nicolab

137.75K€ | Lider

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Duración del proyecto: 12 meses Fecha Inicio: 2020-09-15
Fecha Fin: 2021-09-30

Líder del proyecto

NICOLAB BV No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 138K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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