Jan. 26, 2026 — AISAP, a provider of AI point-of-care diagnostics, has published a new clinical study in the peer-reviewed journal Frontiers in Digital Health. The research provides clinical evidence that AISAP’s deep learning model can accurately detect significant valvular disease and ventricular dysfunction using only a single, focused ultrasound view, even when the images are acquired by non-cardiologists using handheld devices.
The study, "Artificial intelligence assessment of valvular disease and ventricular function by a single echocardiography view," analyzed more than 120,000 echocardiographic studies to train the model, which was then validated against a prospective cohort of patients. By capturing structural and temporal cardiac features across the cardiac cycle, the model demonstrated that AI can identify meaningful signatures of heart disease from standard 2D grayscale clips alone, without the need for traditional and complex modalities such as color flow doppler. The results demonstrated significant diagnostic performance, with the AI achieving an Area Under the Curve (AUC) of up to 0.97 for detecting reduced ejection fraction and 0.95 for right ventricular dysfunction during real-world prospective testing.
"The findings of this study represent a significant shift in how we approach cardiac screening," said Lior Fisher, MD, lead author and physician at the Leviev Cardiovascular Institute at Sheba Medical Center. "By proving that a single-view acquisition can yield such high diagnostic accuracy for major pathologies like heart failure and valvular regurgitation, we are effectively removing the technical barriers to cardiac imaging. This allows a much broader range of clinicians to identify potentially life-threatening conditions at the point of care, long before a patient reaches the echo lab."
In traditional clinical settings, a comprehensive echocardiogram requires a highly trained sonographer, multiple imaging angles, and expert interpretation by a cardiologist, a process that can take days or weeks. This study confirms how AISAP’s technology can bypass these bottlenecks, allowing frontline clinicians in emergency rooms, rural clinics, and internal medicine wards to provide immediate, specialist-grade triage. This capability is especially relevant for the over-65 population, where valvular heart disease prevalence is highest and early detection is critical.
"This validation reflects our commitment to continue advancing what’s possible with AI in healthcare" said Adiel Am-Shalom, CEO and co-founder of AISAP. "By proving that AI can rapidly extract clinically meaningful signatures from minimal ultrasound data, this study confirms that our POCAD platform isn't just a tool for clinicians, it is a potential lifeline for patients. Delivering specialist-level insights from a single view enables timely, bedside decision-making and the immediate detection of heart disease anywhere in the world, from major U.S. health systems to remote rural clinics."
AISAP’s FDA-cleared POCAD platform enables clinicians to perform comprehensive, AI-powered ultrasound diagnostics at the patient's bedside, regardless of setting. While this study’s single-view research informs AISAP’s future innovation pipeline, the company’s current commercially available platform is already used in routine clinical practice to evaluate key cardiac pathologies. This publication follows AISAP's continued collaborations with research partners, expansion into leading health systems, and a growing number of global clinical sites.
Go to www.aisap.ai for more information.
