Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/190190768 |
| Start date: | 01-08-2022 |
| End date: | 31-01-2025 |
| Total budget - Public funding: | 3 574 343,75 Euro - 2 500 000,00 Euro |
Cordis data
Original description
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 requiressubstantial 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.
Status
SIGNEDCall topic
HORIZON-EIC-2021-ACCELERATORCHALLENGES-01-01Update Date
09-02-2023
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