Ashley P. Akerman, Mihaela Porumb, Christopher G. Scott, Arian Beqiri, Agisilaos Chartsias, Alexander J. Ryu, William Hawkes, Geoffrey D. Huntley, Ayana Z. Arystan, Garvan C. Kane, Sorin V. Pislaru, Francisco Lopez-Jimenez, Alberto Gomez, Rizwan Sarwar, Jamie O'Driscoll, Paul Leeson, Ross Upton, Gary Woodward, and Patricia A. Pellikka.
The study evaluated whether EchoGo® Heart Failure could detect HFpEF using only a single apical 4-chamber transthoracic echocardiogram video clip, a view routinely captured during standard echo exams.
This is important because HFpEF detection often requires multiple clinical and imaging inputs, which can be incomplete, discordant, or difficult to interpret. By using a routinely acquired video clip, EchoGo® Heart Failure may help reduce reliance on complex manual measurements and support more consistent identification of patients who need further evaluation.
EchoGo® Heart Failure was trained and validated using data from Mayo Clinic and St George's University Hospitals NHS Foundation Trust, then tested in an independent multisite Mayo Clinic Health System dataset.
In independent testing of 1,284 patients, EchoGo® Heart Failure achieved 87.8% sensitivity and 81.9% specificity, with only 7.3% of studies classified as nondiagnostic because of high model uncertainty.
These findings demonstrate strong diagnostic performance using a single routinely acquired echocardiogram video and support the potential of EchoGo® Heart Failure to help identify HFpEF in clinical settings where diagnosis can be complex or uncertain.
The study compared EchoGo® Heart Failure with established HFpEF clinical scoring systems, including HFA-PEFF and H2FPEF.
Both clinical scores produced high numbers of indeterminate results. EchoGo® Heart Failure correctly reclassified 73.5% of patients with indeterminate HFA-PEFF results and 73.6% of patients with indeterminate H2FPEF results.
This suggests EchoGo® Heart Failure may be particularly useful when conventional diagnostic pathways leave uncertainty.
The study also found that patients classified as HFpEF by EchoGo® Heart Failure had higher age-adjusted mortality than those classified as not suggestive of HFpEF.
This supports the clinical relevance of the model’s output, showing that it was not only detecting imaging patterns, but also identifying patients with worse outcomes.
HFpEF is a complex and heterogeneous condition that can be difficult to diagnose consistently, especially when traditional measurements are incomplete or indeterminate.
This study supports the potential of AI-enabled echocardiography to help automate detection, reduce diagnostic uncertainty, and support more timely identification of patients who may benefit from further assessment and treatment.
Akerman AP, Porumb M, Scott CG, et al. Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence. JACC Advances. 2023;2(6):100452.