Anumana has received 510(k) clearance from the US Food and Drug Administration (FDA) for its ECG-AI pulmonary hypertension (PH) detection algorithm, a software-as-a-medical-device (SaMD).
The algorithm is designed to identify early signs of PH using standard 12-lead electrocardiograms (ECGs). It is now accessible in a wide range of care settings.
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A progressive pulmonary vascular disease, PH is estimated to affect up to 1% of the worldwide population.
Anumana’s PH algorithm identifies subtle abnormalities in ECGs, supporting clinicians in deciding when follow-up diagnostic testing, such as echocardiography, should be pursued within current clinical workflows.
It integrates directly with electronic health record (EHR) systems and ECG management platforms, operating entirely within the health system environment without external patient data transfer.
Anumana COO and president Simos Kedikoglou said: “The FDA clearance of our PH algorithm is the result of rigorous clinical development and regulatory work, and it marks a meaningful step toward expanding access to AI-enabled insights at the point of care.
“As the first PH algorithm cleared for use with standard 12-lead ECGs, it is broadly accessible across care settings, integrates directly into existing clinical workflows, and supports clinical decision-making in real time, with the potential to help identify patients earlier in their disease course. This milestone reflects Anumana’s broader vision to expand the role of ECGs in identifying cardiovascular risk earlier and at scale.”
Developed using data from more than 250,000 de-identified Mayo Clinic patient records, the algorithm was validated in a multi-centre study involving 21,066 patients at five US health systems. In this study, the ECG-AI detected PH with a 73% sensitivity and a 74.4% specificity among adults presenting with dyspnoea.
Mayo Clinic, which co-founded Anumana, holds a financial interest in the company.
In January 2025, Anumana partnered with AliveCor for the development of its AI algorithms intended for the early identification of cardiac diseases.
