ECG-AI Cardiac Amyloidosis (CA) 12-Lead Algorithm
Overview
The Anumana ECG-AI Cardiac Amyloidosis (CA) 12-Lead Algorithm is a software as a medical device (SaMD) designed to assist clinicians in identifying adult patients who may be at risk for Cardiac Amyloidosis (CA) using a standard 12-lead electrocardiogram (ECG).
- What it does: This AI-enabled algorithm analyzes ECG waveforms to detect patterns associated with CA that may not be apparent through human interpretation. It is intended to aid in earlier detection of CA in adults being evaluated for signs or symptoms consistent with the condition, including those with unexplained heart failure, nephrotic syndrome, peripheral neuropathy, arrhythmias, or aortic stenosis.
- Who it is for: The tool is relevant for various specialties and care settings where patients are evaluated for cardiac symptoms, particularly those with comorbidities associated with CA. This includes cardiologists, primary care physicians, and other clinicians in hospital or outpatient settings.
- How it fits a clinical or practice workflow: The algorithm integrates into existing clinical workflows by utilizing standard 12-lead ECGs that are routinely obtained in practice, without requiring additional testing. It is not intended as a standalone diagnostic tool but rather to support clinicians in identifying patients who may benefit from further evaluation for CA.
- Notable capabilities: The ECG-AI CA algorithm was developed at Mayo Clinic and validated in a multi-center study. It is the first and only FDA-cleared algorithm for this indication using standard 12-lead ECGs.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-driven analysis of standard 12-lead ECGs
- Detects cardiac disease signals invisible to the human eye
- Integrates with existing clinical workflows and EHRs
- Aids in earlier detection of Cardiac Amyloidosis
- Software as a Medical Device (SaMD)
- Clinically validated with extensive evidence base
- Supports clinical decision-making at the point of care
- Eligible for reimbursement
Use Cases
- Early identification of cardiac amyloidosis in adults being evaluated for consistent signs or symptoms
- Screening patients with conditions commonly associated with cardiac amyloidosis (e.g., unexplained heart failure, nephrotic syndrome, peripheral neuropathy, arrhythmias, aortic stenosis)
- Supporting clinicians in recognizing suspicion of cardiac amyloidosis from routine ECGs
- Guiding further clinical evaluation for at-risk patients
What Physicians Need to Know
This AI-powered ECG algorithm serves as a valuable screening tool to flag patients at risk for cardiac amyloidosis, especially those with non-specific symptoms or comorbidities like unexplained heart failure, nephrotic syndrome, peripheral neuropathy, arrhythmias, or aortic stenosis. A positive result warrants further clinical evaluation, but a negative result in a high-risk patient should not rule out additional investigation. It's crucial to integrate this tool with clinical judgment and existing guidelines, as it is not a stand-alone diagnostic device. The ability to detect CA earlier, even before advanced imaging, can significantly impact patient outcomes by enabling timely interventions.
The ECG-AI Cardiac Amyloidosis 12-Lead Algorithm is designed as a software-as-a-medical-device (SaMD) that integrates into existing clinical workflows by analyzing standard 12-lead ECG data already gathered in practice, without requiring additional testing. It is built on a secure, clinically validated infrastructure to support seamless integration across diverse healthcare systems. The algorithm was developed in collaboration with Mayo Clinic and has been validated in large, independent, multi-center studies across various U.S. health systems. Anumana is actively pursuing regulatory approval for this algorithm in the USA, Europe, and Japan.
Details
| Category | Cardiology AI, Clinical Decision Support & Reference |
| Pricing | Unknown — unknown |
| Deployment | Integrated with existing ECG management systems and EHR environments |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Unknown AI-estimated Anumana's ECG-AI Cardiac Amyloidosis 12-Lead Algorithm received U.S. Food and Drug Administration (FDA) clearance on April 8, 2026. It is the first and only FDA-cleared device for this indication using standard 12-lead ECGs, and it previously received FDA Breakthrough Device Designation. |
| Integrations | |
| EHR | Not specified |
| Specialties | Cardiology, Hospital Medicine, Internal Medicine |
What the Web Says
The ECG-AI Cardiac Amyloidosis (CA) 12-Lead Algorithm by Anumana is an FDA-cleared AI medical device designed to detect patterns indicative of cardiac amyloidosis from routine 12-lead ECGs. This algorithm aims to address the historical challenge of late-stage diagnosis for cardiac amyloidosis, particularly ATTR-CA, by identifying subtle waveform signatures often missed by the human eye. Its early detection capabilities are crucial given the existence of new disease-modifying therapies that are most effective when initiated early in the disease progression.
Overall: PositiveStrengths
- Early detection of cardiac amyloidosis, which is often underdiagnosed until advanced stages.
- Identifies subtle ECG waveform signatures that human review may miss.
- Potentially enables earlier initiation of disease-modifying therapies for cardiac amyloidosis.
- Can be integrated into existing ECG reporting systems, offering a zero-friction screening addition.
- Relevant for cardiologists, internists, and primary care physicians who order or review ECGs.
- Received FDA 510(k) clearance and Breakthrough Device designation.
Limitations
- No graphical user interface (GUI) of its own, requiring integration with third-party software for result display.
- Specific physician, healthcare IT, tech reviewer, Reddit, G2, or Capterra reviews are not readily available in the search results.
- As an SDK/software module, its effectiveness and user experience are dependent on the integration quality with existing systems.
- The search results do not contain information about pricing or implementation costs.
- No direct user testimonials or case studies were found in the provided search snippets.
- The search results do not contain information about potential false positives or negatives.
Based on reviews from: ReachMD, Physician AI Tools, Innolitics, FDA
Last updated: 2026-09-24
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