Tempus receives U.S. FDA 510(k) clearance for Tempus ECG-AF, an AI-based algorithm that identifies patients at increased risk of AFib

07/08/2024

Tempus receives U.S. FDA 510(k) clearance for Tempus ECG-AF, an AI-based algorithm that identifies patients at increased risk of AFib

Tempus AI, Inc. (NASDAQ: TEM), a leader in artificial intelligence and precision medicine, today announced it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its Tempus ECG-AF device that uses AI to help identify patients who may be at increased risk of atrial fibrillation/flutter (AF). This is the first FDA clearance for an AF indication in the category known as “cardiovascular machine learning-based notification software” and paves the way for clinicians to use this innovative algorithm in the care of their patients.

AF, a common cause of stroke, affects millions of people and can be challenging to diagnose. This clearance solidifies Tempus’ innovative approach to offering clinicians AI-based clinical solutions that support the potential for earlier identification of cardiovascular disease and conditions. ECG-AF is the first of a suite of next generation diagnostics that Tempus has designed to identify patients at risk for a variety of cardiovascular conditions.

Read the full press release here.

See our Research in ECG based Cardiology Algorithms:

- rECHOmmend: An ECG-based machine learning approach for identifying patients at increased risk of undiagnosed structural heart disease detectable by Echocardiography: In this study, we developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography.
- Deep neural networks can predict new-onset Atrial Fibrillation from the 12-lead ECG and help identify those at risk of Atrial Fibrillation-related stroke (link): Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF can be predicted, targeted screening could possibly be used to find it early. In this study, we explore if a deep neural network can predict new-onset AF from the resting 12-lead ECG and study whether this prediction may help identify those at risk of AF-related stroke.
- Prospective evidence generation via ECG-AID Study: We have launched a multi-site prospective study with our beta partners to test our algorithms for atrial fibrillation and structural heart disease.
- Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network: We hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. In this study we used ECGs collected over a 34-year period in a large regional health system, to train a deep neural network with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred.

Our Experts

Brandon Fornwalt, MD, PhD

Senior Vice President of Cardiology, Tempus

John Pfeifer, MD, MPH

Vice President of Clinical Cardiology, Tempus