{"id":64090,"date":"2026-06-05T22:04:14","date_gmt":"2026-06-05T22:04:14","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/64090\/"},"modified":"2026-06-05T22:04:14","modified_gmt":"2026-06-05T22:04:14","slug":"applying-ai-assisted-heart-analysis-in-clinical-settings-ai-driven-cardiac-ultrasound-analysis-project-results-in-brief-horizon-cordis","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/64090\/","title":{"rendered":"Applying AI-assisted heart analysis in clinical settings | AI-driven cardiac ultrasound analysis Project | Results in Brief | HORIZON | CORDIS"},"content":{"rendered":"

Artificial intelligence technologies that fit seamlessly into existing cardiovascular clinical practices can save time, resources, and ultimately, lives.<\/p>\n

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\u00a9 onephoto\/stock.adobe.com<\/p>\n

Cardiovascular diseases remain the leading cause of death globally, and echocardiography \u2013 or heart ultrasound \u2013 is one of the most widely used and cost-effective, non-invasive tools for diagnosing heart conditions. Despite its importance however, access to high-quality echocardiography remains limited by several major challenges.<\/p>\n

\u201cTraditional heart ultrasound analysis is highly dependent on specialist expertise and involves a large amount of manual work,\u201d explains project coordinator Neringa Valantin\u0117 from Ligence(opens in new window)<\/a> in Lithuania. \u201cCardiologists often spend the majority of examination time performing repetitive measurements and preparing reports.\u201d<\/p>\n

Valantin\u0117 notes that the process is also prone to variability and human error. Healthcare systems across Europe also face growing pressure from ageing populations, increasing cardiovascular disease rates and shortages of trained medical professionals.<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAI, software engineering and cardiology<\/p>\n

The AI-driven cardiac ultrasound analysis(opens in new window)<\/a> project, which was funded by the European Innovation Council(opens in new window)<\/a> and coordinated by Ligence, sought to address these bottlenecks by improving the speed, consistency, accessibility and quality of heart ultrasound analysis. This involved developing and refining advanced neural network models capable of analysing complex ultrasound image data.<\/p>\n

Significant effort also went into improving the usability, speed, interoperability and reliability of the software platform so it could function effectively in clinical environments.<\/p>\n

\u201cTo achieve our aims, we combined expertise from artificial intelligence, software engineering, cardiology, clinical practice and regulatory affairs,\u201d says Valantin\u0117. \u201cWe worked closely with clinicians and healthcare institutions to ensure that the technology addressed real clinical needs.\u201d<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tClinical evaluation using echocardiography data<\/p>\n

Innovations were assessed through continuous AI<\/a> performance validation, as well as clinical evaluation using real-world echocardiography data and ongoing feedback from clinicians.<\/p>\n

\u201cA key project objective was to build a diverse and representative dataset that would support robust AI performance across different patient populations, clinical environments and ultrasound imaging practices,\u201d remarks Valantin\u0117.<\/p>\n

The platform\u2019s automated measurements and reporting capabilities were also assessed against standard clinical practice; and compared with evaluations performed by experienced clinicians. This helped to ensure clinically reliable and consistent results.<\/p>\n

\u201cWe focused in parallel on the usability of the technology in clinical workflows, as well as regulatory issues to support safe deployment in healthcare environments,\u201d adds Valantin\u0117. \u201cAchieving regulatory compliance in different markets was an important part of the project and a key step towards broader clinical adoption of the technology.\u201d<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tBroader clinical adoption and continued expansion<\/p>\n

One of the key lessons from the project, believes Valantin\u0117, has been that successful AI solutions in healthcare must be designed around clinical realities. \u201cAccuracy alone is not enough \u2013 systems also need to integrate smoothly into hospital workflows, save clinicians time and build trust among users,\u201d she says. \u201cAnother important achievement was obtaining regulatory certification, demonstrating that advanced AI tools can meet stringent medical device requirements.\u201d<\/p>\n

Next steps include working towards broader clinical adoption and continued expansion of the platform\u2019s capabilities. Ligence is also working on scaling deployment across hospitals and healthcare systems internationally, while continuing collaborations with clinicians to refine the technology based on real-world use.<\/p>\n

\u201cThe long-term vision is to make high-quality cardiac ultrasound analysis more accessible, efficient and consistent for patients and healthcare professionals alike,\u201d notes Valantin\u0117.<\/p>\n

\u201cBy automating repetitive tasks and supporting clinicians with AI-driven analysis, technologies such as Ligence Heart(opens in new window)<\/a> have the potential to reduce waiting times, improve diagnostic quality and enable earlier detection of cardiovascular disease. This could ultimately lead to better patient outcomes and more sustainable healthcare systems.\u201d<\/p>\n

\u201cThe added value of N-Spire lies in its innovative approach to microorganism cultivation, transforming agricultural residues into high-value microbial products through a manufacturing platform,\u201d concludes Miles. \u201cBy combining continuous pre-thermal treatment and solid-state fermentation, the project demonstrated a more circular, lower-cost and potentially lower-carbon alternative to conventional fertiliser and microbial production systems.\u201d<\/p>\n

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tDiscover other articles in the same domain of application<\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence technologies that fit seamlessly into existing cardiovascular clinical practices can save time, resources, and ultimately, lives.…\n","protected":false},"author":2,"featured_media":64091,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,36317,25,36318,27226,36319,49,2649,36320],"class_list":["post-64090","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-ai-driven-cardiac-ultrasound-analysis","tag-artificial-intelligence","tag-cardiovascular","tag-clinical","tag-echocardiography","tag-healthcare","tag-heart","tag-ultrasound"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/64090","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=64090"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/64090\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/64091"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=64090"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=64090"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=64090"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}