AISAP, a leader in AI-powered point-of-care diagnostics, announced the publication of a new clinical study in the peer-reviewed journal Frontiers in Digital Health, providing strong evidence that its deep learning model can accurately identify major valvular disease and ventricular dysfunction using only a single focused ultrasound view. Remarkably, the AI achieves high accuracy even when images are captured by non-cardiologists with handheld devices.
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The study, titled “Artificial intelligence assessment of valvular disease and ventricular function by a single echocardiography view,” analyzed more than 120,000 echocardiographic studies to train the AI model, which was then validated on a prospective patient cohort. By capturing structural and temporal cardiac features throughout the cardiac cycle, the AI detected meaningful signatures of heart disease from standard two-dimensional grayscale clips without relying on complex imaging modalities such as color flow Doppler. The results demonstrated exceptional diagnostic performance, with the AI achieving an area under the curve of 0.97 for reduced ejection fraction and 0.95 for right ventricular dysfunction in real-world testing.
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Lior Fisher, MD, lead author of the study and physician at the Leviev Cardiovascular Institute at Sheba Medical Center, described the findings as transformative for cardiac screening. He explained that single-view acquisition with high diagnostic accuracy removes technical barriers to echocardiography, allowing a broader range of clinicians to detect life-threatening conditions at the point of care well before patients reach the echo lab.
Traditional echocardiograms require highly trained sonographers, multiple imaging angles, and expert interpretation by cardiologists, often taking days or weeks to complete. AISAP’s technology streamlines this process, enabling frontline clinicians in emergency departments, rural clinics, and general wards to provide immediate, specialist-grade assessment. Early detection is particularly critical for the population over 65, where valvular heart disease is most common.
Adiel Am-Shalom, CEO and Co-Founder of AISAP, highlighted that the study reinforces the company’s mission to advance AI in healthcare. He said that by extracting clinically meaningful insights from minimal ultrasound data, the POCAD platform provides not only a diagnostic tool for clinicians but also a potential lifeline for patients. The ability to deliver specialist-level analysis from a single ultrasound view allows timely bedside decisions and early detection of heart disease across healthcare settings from major hospitals to remote clinics.
AISAP’s FDA-cleared POCAD platform already supports clinicians in performing comprehensive, AI-driven cardiac evaluations at the bedside. While this single-view study informs the company’s future innovations, the platform is currently in routine clinical use for evaluating key cardiac conditions. The publication follows ongoing research collaborations, expansion into leading health systems, and adoption at an increasing number of global clinical sites.
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