Published Scientific Evidence
Category: (Hyperkalemia)
Anumana’s ECG-AI™ technology is supported by one of the most extensive evidence bases in cardiovascular AI. This library provides direct access to our peer-reviewed validation studies and broader body of clinical research.
August
25
2025
August 25, 2025
CJASN
Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings
Abstract: Key Points • Measuring blood potassium has always required access to blood. The surface electrocardiogram, analyzed using an artificial intelligence algorithm, can detect hyperkalemia bloodlessly. • The artificial intelligence-analyzed…
April
03
2019
April 03, 2019
JAMA Cardiology
Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram
For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables…
August
31
2017
August 31, 2017
Journal of Electrocardiology
Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone
We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring.…
January
24
2016
January 24, 2016
Journal of the American Heart Association
Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG
Hyper- and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood-free method for tracking potassium would be an important…