Health Science Reviews

by Ramses Peigou Wonkam

Introduction:
Have you ever wondered if we could spot a sneaky heart rhythm that often goes unnoticed but could lead to serious complications? Well, researchers have now unlocked a new method to catch this rhythm in its tracks, using the power of A.I.

The Problem:
Atrial fibrillation, a tricky little heart irregularity, often slips under the radar, increasing the risk of stroke and heart complications. Traditional methods like the ECG sometimes miss it, and without a confirmed diagnosis, treatments can be risky. This has been a long-standing challenge in heart care, with many strokes occurring without any clear reason, possibly due to this undetected irregularity.

The Brilliant Idea:
Researchers decided to tap into the advancements of machine learning. They believed that although atrial fibrillation might be playing hide and seek, it does leave behind some clues. By training an AI on ECGs, they hoped to spot these clues.

The Method:
The team gathered heart readings (ECGs) from a whopping 180,922 patients from the Mayo Clinic spanning decades! They then trained their AI model on a subset of this data and tested it on the rest. The primary goal was to see if the AI could identify atrial fibrillation even when the heart seemed to be beating just fine.

The Results:
The AI model did a stellar job! On its initial run, it detected atrial fibrillation with an accuracy of around 79.4%. But when the team decided to give the model more data from a patient’s first month, the accuracy shot up to an impressive 83.3%!

The Big Picture:
This is not just a win for technology but a giant leap for heart care. Early detection of atrial fibrillation means timely treatment, potentially saving countless lives. The marriage of AI and medical science seems to be a match made in heaven, and this study is a testament to that.

Reference:
AVa ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019 Sep 7;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0. Epub 2019 Aug 1. PMID: 31378392.