Medicalgorithmics’ AI Algorithms can Predict Atrial Fibrillation Risk

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Medicalgorithmics’ AI Algorithms can Predict Atrial Fibrillation Risk

Medicalgorithmics’ Chief Scientific Officer, Linda Johnson, MD, PhD recently presented an abstract at the American College of Cardiology Scientific Session in New Orleans. The research showcased the importance of AI-based predictive analytics and how Medicalgorithmics’ DeepRhythmAI platform can predict the likelihood of atrial fibrillation.

Dr. Linda Johnson, an Associate Professor at Lund University, utilized a large dataset obtained from the Medicalgorithmics PocketECG device to determine whether 24 hours of ECG monitoring can predict the chance of developing atrial fibrillation over a 30 day period. The study found that by using data from 24-hour ECG monitoring, long-term ECG monitoring can safely be avoided in 20% of the patient population. Longer-term monitoring is warranted for the remaining 80% of individuals who have an AF incidence of over 9%.

“Our predictive algorithm gives us the ability to rationalize monitoring for atrial fibrillation. We can use this to shorten monitoring in those who don’t need it, but also find those with atrial fibrillation risk who need extended monitoring. This is important from a cost-effectiveness perspective, but also to help improve health outcomes related to atrial fibrillation,” says Linda Johnson, MD, Phd, who is Chief Scientific Officer of Medicalgorithmics.

The results of this study showcase Medicalgorithmics’ cutting-edge DeepRhythmAI platform and highlight the company’s commitment to being at the forefront of medical innovation.

“The scientific research conducted at Medicalgorithmics has enabled the development of a new generation algorithm for diagnosing atrial fibrillation, which reduces the burden on medical personnel and monitoring time for 20% of patients. In countries where the standard for atrial fibrillation diagnosis is 24 hours or a 3-day Holter, our software provides a unique opportunity for the remaining 80% of patients. It allows for the identification of individuals at risk of atrial fibrillation who require additional diagnostics, and for whom standard ECG monitoring is too short. We have a very large set of medical data that allows us to conduct R&D work in the area of AI for new predictive models. Our new algorithms will enable predictive analysis, biomarker identification, and health analysis to optimize the process of diagnosing and treating patients with cardiovascular diseases,” emphasizes Przemysław Tadla, Chief Technology Officer at Medicalgorithmics.

One in four adults over the age of 40 develops atrial fibrillation (AF) during their lifetime. Medicalgorithmics is excited to offer a new, less burdensome approach to AF diagnosis.

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