Anumana, a leader in cardiovascular AI, announced U.S. Food and Drug Administration (FDA) clearance of its ECG-AI algorithm for cardiac amyloidosis (CA) – the first and only cleared for this indication using standard 12-lead electrocardiograms (ECGs). The AI-enabled software-as-a-medical-device (SaMD), previously granted FDA Breakthrough Device Designation and selected among the first 15 devices in FDA’s Total Product Life Cycle Advisory Program pilot, is designed to help clinicians identify patients who may be at risk for CA at the point of care.
Cardiac amyloidosis is a serious, life-threatening condition caused by abnormal protein deposits in the heart that can lead to heart failure if not recognized early. It is frequently underdiagnosed due to nonspecific symptoms that overlap with common cardiac conditions. Early diagnosis is critical, and timely intervention can meaningfully improve outcomes for patients. While a standard ECG is routinely obtained during the evaluation of these symptoms, human interpretation often misses the subtle combinations of features that may indicate CA. With FDA clearance, Anumana’s AI-driven software-as-a-medical-device (SaMD) is now available for clinical use, enabling analysis of ECG data to help identify patients who may benefit from further evaluation.
“Cardiac amyloidosis can be challenging to detect early, especially when its signs overlap with more common heart conditions,” said Martha Grogan, M.D., consultant in Cardiovascular Medicine at Mayo Clinic and co-principal investigator of the clinical study. “A tool that helps clinicians recognize suspicion of amyloidosis from a routine ECG could support earlier diagnosis and more timely next steps in care.”
Initially developed at Mayo Clinic, Anumana’s ECG-AI CA model was subsequently validated in a large, independent, multi-center study of 25,525 patients across four U.S. health systems. ECG-AI detected CA with 78.9% sensitivity and 91.2% specificity in adult patients presenting with signs, symptoms, or comorbidities of CA. This strong performance may support more efficient use of confirmatory testing and earlier intervention.
“What makes this work especially meaningful is the rigor of the validation,” said Angela Dispenzieri, M.D., hematologist at Mayo Clinic and co-principal investigator of the clinical study. “This ECG-AI algorithm was validated in a large multicenter study that included both ATTR and AL cardiac amyloidosis at major referral centers with deep expertise in amyloidosis diagnosis, supporting its potential to help identify patients earlier.”
“Each of our FDA-cleared algorithms addresses a specific and frequently missed cardiovascular condition, and cardiac amyloidosis represents an important addition to that portfolio,” said Maulik Nanavaty, CEO of Anumana. “The more conditions we can identify from a single ECG, the more valuable the test becomes in clinical practice. That’s what Anumana is working toward with each new clearance as we continue to advance our rigorous clinical evidence approach.”
Anumana’s algorithm analyzes ECG waveforms to detect patterns associated with CA that are often not visible to the human eye. By leveraging ECGs already obtained in clinical practice, Anumana’s solutions integrate directly into existing workflows without requiring additional testing, helping clinicians identify at-risk patients and determine next steps.
This clearance builds on Anumana’s portfolio of FDA-cleared ECG-AI algorithms, which also includes solutions for low ejection fraction and pulmonary hypertension. Additional algorithms are currently being developed to further expand its portfolio.
