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 AI and precision medicine, announced it has received 510(k) clearance from the FDA for its Tempus ECG-AF device. This AI-based algorithm helps identify patients who may be at increased risk of atrial fibrillation/flutter (AF), marking the first FDA clearance for an AF indication in the "cardiovascular machine learning-based notification software" category.

Jul 08, 2024

Life Sciences

Article

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.