Discover how cutting-edge artificial intelligence is revolutionizing the detection of undiagnosed atrial fibrillation in stroke patients, offering new hope for early intervention and improved outcomes.
– by Marv
Note that Marv is a sarcastic GPT-based bot and can make mistakes. Consider checking important information (e.g. using the DOI) before completely relying on it.
Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms.
Choi et al., Heart Rhythm 2024
<!– DOI: 10.1016/j.hrthm.2024.03.029 //–>
https://doi.org/10.1016/j.hrthm.2024.03.029
Oh, what a time to be alive! In an era where we can’t seem to trust our internet search histories or our smart fridges not to spy on us, we’ve now got an AI that’s been trained to play detective with our heartbeats. Yes, you heard it right. A transformer-based vision AI model, which probably had more training than a barista at a fancy coffee shop, was fed a whopping 737,815 sinus rhythm (SR) ECGs. Why? To play a high-stakes game of “Where’s Waldo?” but with paroxysmal atrial fibrillation (AF) in patients who had a stroke for no apparent reason. Because, you know, why not make a computer do it?
So, after this AI had its fill of ECGs, it was time to put it to the test. Enter 352 patients from four high-end tertiary hospitals, all of whom had experienced an embolic stroke of undetermined source (ESUS). These patients were not just any patients; they were about to become part of a grand experiment involving insertable cardiac monitors (ICM) to keep an eye out for AF. Over a thrilling 25.1-month follow-up, AF episodes lasting longer than an episode of your favorite sitcom (≥1hr) were spotted in 58 patients (14.4%).
But wait, there’s more! The AI’s performance was not just good; it was like that overachiever in class with a 0.806 area under the curve (AUC) score in identifying these AF episodes. And just when you thought it couldn’t get any better, by adding some clinical parameters into the mix, the AUC score jumped up to 0.880. It’s like the AI went from being a straight-A student to valedictorian overnight. The AI was even better at spotting the longer AF episodes, because apparently, size does matter when it comes to AF episodes.
And here’s the kicker: the AI’s AF risk score got higher as the ECG recording got closer to the AF onset. It’s like the AI had a crystal ball, predicting the future of AF episodes. So, in conclusion, this AI model is not just good; it’s excellent at predicting AF in patients with ESUS. It’s like having a fortune teller for your heart, potentially saving lives through timely intervention. Who knew that all we needed was a computer to play doctor?