Ultromics and Mayo Clinic partner up for AI diagnosis of heart failure

The joint UK-US scientific research team will use AI and machine learning to predict and detect heart failure.
By Sara Mageit
06:38 am

A research programme led by US-based non-profit medical centre Mayo Clinic and UK health tech firm Ultromics will apply AI to forecast heart failure.

The team will use AI analysis of ultrasound heart scans to identify the markers of heart failure and alert doctors to potential heart failure.


The aim is to develop a diagnostic and predictive tool that can reduce misdiagnosis and enable its earlier prevention.

By supporting medical professionals, it will free up their time to provide patient care and ease the pressure on care teams.

Globally, heart failure affects at least 26 million people and is increasing in prevalence, according to the Global Public Health Burden of Heart Failure report.

Worldwide, it is the leading cause of hospitalisation in people over the age of 65. 


Earlier this year, Ultromics scored $10m for its cardiac decision support tool with participation from Mayo clinic, only two years after the startup scored £10m in a Series A round.

In related news, pharma company AstraZeneca recently signed a deal with digital health company Eko to develop digital health-screening tools and use AI tech for clinical trials.


CEO and co-founder of Ultromics, Dr Ross Upton said, “This project is focused on a critical aspect of cardiac disease as it affects so many people every day. Using our pioneering AI technology stack, our objective is to map and scan databases of ultrasound images and develop detailed models to diagnose and hopefully even predict heart failure. Early intervention can make a huge difference to a patient’s treatment and quality of life – so the sooner we can identify the condition, the better.”

Dr Upton explained: “The study has two key objectives: the first is to identify novel biomarkers that can help identify early signs of heart failure. And the second is to develop a machine learning model using the novel biomarkers to provide an automated risk prediction of heart failure at the point of care.”


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