Cardiologists use echocardiography to diagnose a range of functional or structural abnormalities of the heart. Using often over 100 videos and images that capture different parts of the heart, echocardiographers make dozens of measurements, such as the heart's size and shape, ventricle thickness, and the movement and function of each heart chamber, to assess patient heart health. Now, a new study has shown that an artificial intelligence (AI)-enabled tool can interpret echocardiograms with a high degree of accuracy in just a few minutes.

In the study led by Yale School of Medicine (YSM, New Haven, CT, USA), the researchers found that the AI tool, PanEcho, could perform 39 diagnostic tasks based on multi-view echocardiography and accurately detect conditions such as severe aortic stenosis, systolic dysfunction, and left ventricle ejection fraction, among others. This study builds on previous publications that demonstrated the technology’s accuracy. PanEcho was developed using 999,727 echocardiographic videos collected from Yale New Haven Health patients between January 2016 and June 2022. Researchers then validated the tool using studies from 5,130 Yale New Haven Health patients as well as three external data cohorts. To validate the model’s accuracy with point-of-care ultrasounds, the researchers used imaging from the Yale New Haven Hospital emergency department, which performs point-of-care ultrasounds as part of routine care.

While PanEcho is not yet available for clinical use, the paper published in JAMA discusses several potential future clinical applications of the technology. For instance, echocardiographers could utilize the tool as a preliminary reader to help assess images and videos in the echocardiography lab. It could also serve as a second set of eyes to help identify potentially missed abnormalities in existing databases. The researchers also note that this technology could be particularly valuable in low-resource settings, where access to equipment and skilled echocardiographers is limited. In these environments, clinicians often rely on handheld, point-of-care ultrasound devices, which produce lower-quality imaging that can be more challenging to interpret. The team is now working to conduct studies to assess how using the tool might change patient care in the echocardiography laboratory at Yale. The full model and weights are available via open source, and the research team is encouraging other investigators to test the model using their echocardiographic studies and make improvements.

“The tool can now measure and assess a wide range of heart conditions, making it much more attractive for future clinical use,” said Evangelos K. Oikonomou, MD, DPhil, clinical fellow (cardiovascular medicine) and co-first author of the study. “While it is highly accurate, it can be less interpretable than the read from a clinician. It’s still an algorithm and it requires human oversight.”

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