ECG-AI Pulmonary Hypertension (PH) 12-Lead algorithm
Overview
The ECG-AI Pulmonary Hypertension (PH) 12-Lead algorithm, developed by Anumana, Inc., is an innovative artificial intelligence tool designed to assist clinicians in the early identification of pulmonary hypertension. This algorithm analyzes standard 12-lead electrocardiogram (ECG) recordings, a widely available and non-invasive diagnostic test, to detect subtle patterns indicative of PH.
Pulmonary hypertension is a serious and progressive condition that often goes undiagnosed until advanced stages, leading to poorer patient outcomes. By leveraging AI, Anumana’s algorithm aims to provide a rapid and accessible screening tool that can prompt earlier investigation and intervention, potentially improving the prognosis for affected individuals. The algorithm processes digital ECG data to generate a risk score or flag, alerting physicians to the potential presence of PH.
This technology represents a significant step forward in utilizing AI for cardiovascular diagnostics, transforming routine ECGs into a more powerful screening tool. Its integration into clinical workflows could enable more proactive management of PH, reducing diagnostic delays and facilitating timely referrals to specialists for definitive diagnosis and treatment.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered 12-lead ECG analysis
- Detection of pulmonary hypertension indicators
- Non-invasive screening tool
- Potential for early diagnosis
- Integration with existing ECG workflows
- Risk stratification for PH
Use Cases
- Screening for pulmonary hypertension in at-risk populations
- Assisting primary care physicians in identifying PH
- Supporting cardiologists and pulmonologists in diagnostic pathways
- Reducing diagnostic delays for pulmonary hypertension
What Physicians Need to Know
This AI algorithm serves as a screening tool to aid in the earlier detection of pulmonary hypertension and should be used in conjunction with clinician judgment. [2, 3, 4, 6] A positive result suggests the need for further clinical evaluation, such as echocardiography. [3, 5, 11] A negative result, especially in high-risk patients, should not rule out additional clinical evaluation. [3] The algorithm is not intended to be a stand-alone diagnostic device for PH or to replace current clinical practice guidelines. [3] It should not be used on ECGs from adults with an implanted pacemaker or on ECGs with a paced rhythm. [3]
The ECG-AI PH 12-Lead algorithm integrates with Electronic Health Record (EHR) systems, including ECG management platforms. [5, 11] It operates entirely within the health system environment, ensuring patient data is not transferred externally. [5, 11] This allows for broad accessibility across care settings and seamless integration into existing clinical workflows. [5, 7, 11]
Details
| Category | Cardiology AI, Clinical Decision Support & Reference, Lab & Diagnostics |
| Pricing | Unknown — unknown |
| Deployment | Integrates with EHR systems, including ECG management platforms, and runs entirely within the health system environment without transferring patient data. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Cleared AI-estimated Received U.S. Food and Drug Administration (FDA) 510(k) clearance on March 28, 2026, for its pulmonary hypertension (PH) algorithm, an AI-enabled software-as-a-medical-device (SaMD) that detects early signs of PH. |
| Integrations | |
| EHR | Not specified |
| Specialties | Cardiology, Internal Medicine, Pulmonology |
Social Proof
| Customers | unknown |
| Notable | Mayo Clinic (co-founder and financial interest)Vanderbilt University Medical Center (involved in validation studies)Janssen Research & DevelopmentLLC (involved in development). |
What the Web Says
The ECG-AI Pulmonary Hypertension (PH) 12-Lead algorithm is a software designed to assist in the early detection of elevated mean pulmonary arterial pressure (mPAP), an indicator of PH, in adults experiencing shortness of breath. It utilizes artificial intelligence and deep learning to analyze standard 12-lead electrocardiograms (ECGs) for subtle patterns associated with PH that may not be apparent to the human eye. The algorithm has received FDA clearance and is intended to be broadly accessible and integrate into existing clinical workflows to accelerate diagnosis and treatment, potentially improving patient outcomes.
Overall: PositiveStrengths
- Aids in earlier detection of PH, which is often diagnosed late due to non-specific symptoms.
- Utilizes standard 12-lead ECGs, making it broadly accessible and non-invasive.
- Integrates directly into existing clinical workflows and EHR systems.
- Demonstrated high discriminative ability in identifying PH-likely patients, with AUCs ranging from 0.88 to 0.92 at diagnosis in validation studies.
- Can detect PH months and even years before clinical diagnosis, potentially allowing for earlier intervention.
- Outperformed manual physician interpretation of ECGs in identifying PH-related abnormalities.
Limitations
- Not intended to be a standalone diagnostic device for PH or replace current clinical practice guidelines.
- A negative result should not rule out additional clinical evaluation if the patient is at high risk for PH.
- Should not be used on ECGs from adults with an implanted pacemaker or paced rhythm.
- Some general concerns exist regarding the use of large language models (LLMs) for ECG interpretation, though this specific algorithm is a specialized machine learning model.
- The algorithm was developed using de-identified patient records from Mayo Clinic, which co-founded the developer (Anumana) and has a financial interest in the company.
- Future studies will focus on multicenter validation in more diverse cohorts and different types of PH.
Based on reviews from: Mayo Clinic, European Respiratory Journal, Patient.info, Healio, Medical Device Network, MDPI, PMC - NIH, Anumana AI, ClinicalTrials.gov, Medical Economics, Reddit
Last updated: 2026-08-19
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