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AI-driven cardiac ultrasound analysis

Project description

Development of AI-assisted analysis of heart ultrasound images

Heart ultrasound is an accessible, widely used, and cost-effective heart imaging method. However, interpretation of the acquired images requires advanced skills and is error-prone and vulnerable to inconsistencies. Funded by the European Innovation Council, the AI-driven cardiac ultrasound analysis project aims to automate the whole process of heart ultrasound analysis using AI-driven technology. The developed approach will seamlessly integrate with existing infrastructure in hospitals, enabling images and results to load onto hospital networks. The introduction of this new technology will dramatically enhance the quality of analysis, as well as allow for earlier diagnosis, better risk stratification and patient management.

Objective

Heart ultrasound is the most versatile, most widely used, and cost-effective heart imaging method. Accessibility to ultrasound imaging is growing rapidly as the devices are getting cheaper and smaller. However, interpretation of the acquired images creates a bottleneck; it requires
substantial skill, it is long, manual, and prone to errors and variability. Ligence is remodelling the quality, difficulty, and length of echocardiography with an AI-driven tool to automate the whole analysis of heart ultrasound images. Deep learning neural networks classify heart image
views, detect heart cycle phases, and perform measurements. It seamlessly integrates with existing infrastructure in hospitals, meaning that moments after images are loaded onto the hospital's network the results are accessible on any workstation. This results in dramatically increased
accessibility and analysis quality, earlier diagnosis, and better patient risk stratification, monitoring, and patient management.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-EIC-ACC-BF - HORIZON EIC Accelerator Blended Finance

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Call for proposal

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(opens in new window) HORIZON-EIC-2021-ACCELERATORCHALLENGES-01

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Coordinator

LIGENCE UAB
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.

€ 2 500 000,00
Address
TAIKOS PR. 54
LT-51305 KAUNAS
Lithuania

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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

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 3 574 343,75
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