<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-07-08T08:44:08Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/8DBE3101-6074-4728-A8B6-9A7F9394424C" ns1:id="8DBE3101-6074-4728-A8B6-9A7F9394424C"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/A8968423-6CD1-4D7F-82CC-AA8C3E83CC59" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/93E8F4D5-E0EF-4B21-A6EF-5D119E762C6A" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/93E8F4D5-E0EF-4B21-A6EF-5D119E762C6A" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-09-29T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/6892DFAF-7177-4711-A349-715D13B90B86" ns1:rel="FUND" ns1:start="2025-09-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10162917</ns2:identifier></ns2:identifiers><ns2:title>Artificial Intelligence Electrocardiography platform for detection and prediction of cardiovascular and non-cardiovascular diseases</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Our project aims to transform the humble electrocardiogram (ECG) from a simple diagnostic tool into a powerful predictive instrument through artificial intelligence. Despite being the leading global cause of death, cardiovascular disease often remains undetected until serious events occur, with current risk prediction tools showing limited accuracy. Our innovation addresses this critical healthcare gap by enabling early intervention before symptoms appear.

We have developed a suite of AI-enhanced ECG models that can analyse a standard heart tracing and diagnose as well as predict a range of future health outcomes with remarkable precision. This technology doesn't just identify existing heart conditions---it forecasts the likelihood and timing of future cardiovascular events, essentially allowing doctors to &amp;quot;see into the future&amp;quot; of a patient's heart health.

What makes our approach unique is its versatility in processing both digital ECG signals and photographs of paper ECGs. This flexibility enables deployment across all healthcare settings, from advanced hospitals with modern equipment to community clinics still using traditional paper records. No additional hardware investment is required, making the technology accessible and cost-effective.

The platform builds upon extensive research validated across five million ECGs from four continents, demonstrating superior predictive accuracy compared to conventional risk assessment methods. Our models can predict mortality risk, future heart failure, arrhythmias, heart attacks, and even non-cardiac conditions like diabetes---all from a single ECG that takes just minutes to perform.

For healthcare providers, our technology enables more efficient resource allocation by identifying which patients need urgent intervention and which can be safely monitored. For patients, the early detection of heart disease risk provides opportunities for lifestyle changes and preventative treatments that could significantly reduce life-threatening events.

This project will develop two essential components needed to bring our validated AI models into clinical practice: a secure cloud analysis infrastructure compliant with healthcare data standards and regulation as a medical device to allow deployment in the UK.

By transforming standard ECGs into comprehensive risk assessment tools, our innovation aligns perfectly with the NHS Long Term Plan's emphasis on cardiovascular disease prevention. The resulting system will enable healthcare providers to shift resources toward prevention as populations age and cardiovascular disease burden increases, ultimately saving lives through earlier intervention and more personalised patient care.</ns2:abstractText></ns2:project>