COVID-19 fatalities may have captured more attention lately, but heart disease remains the leading cause of death in the U.S. More than 300,000 Americans this year will die of sudden cardiac arrest (also called sudden cardiac death, or SCD), where the heart stops functioning properly.
These events occur suddenly and often without warning, making them nearly impossible to predict. But that may be changing, thanks to 3D imaging and artificial intelligence (AI) technology under study at Johns Hopkins University.
There, researchers are working to develop a way to create more accurate and personalized models of the heart — and not just any heart, your heart, if you’re a patient in need.
“Right now, a clinician can only say whether a patient is at risk or not at risk for sudden death,” says Dan Popescu, PhD, a Johns Hopkins research scientist and first author of a new study on AI’s ability to predict sudden cardiac arrest survival. “With this new technology, you can have much more nuanced predictions of probability of an event over time.”
Put another way: By harnessing AI, clinicians may be able to not only predict if a patient is at risk, but also when an event is most likely to occur. And they can do this by having a much clearer and more personalized look at the electrical “wiring” of your heart.
Your Heart, the Conductor
Your heart isn’t just a metronome responsible for keeping a steady stream of blood pumping to tissues with every beat. It’s also a conductor through which vital energy flows.
To make the heart beat, naturally occurring electrical impulses flow from top to bottom. Healthy heart cells relay this electricity seamlessly. But in a heart damaged by inflammation or a past event (like a heart attack), scar tissue will inhibit the energy flow.
When an electrical impulse encounters a scarred area, the signal can become erratic, disrupting the set top-to-bottom path and causing irregular heartbeats (arrhythmias), which increase one’s danger of sudden cardiac death.
Seeing the Heart in 3D
Today’s available diagnostics offer some insights into the heart’s makeup. For example, magnetic resonance imaging (MRI) scans can reveal damaged areas. Positron emission tomography (PET) scans can show inflammation. And electrocardiograms (EKGs) can record the heart’s electrical signals from beat to beat.
But all these technologies offer only a snapshot, showing heart health at a moment in time and unable to predict future events. Which is why scientists at Johns Hopkins are going further to develop 3D digital replicas of a person’s heart, known as computational heart models.
Computational models are computer-simulated replicas that combine mathematics, physics, and computer science. These models have been around for a long time and can be used in many fields ranging from manufacturing to economics.
In heart medicine, these models are populated with digital “cells,” which imitate living cells and can be programmed with different electrical properties depending on whether they are healthy or diseased.
“Currently available imaging and testing (MRIs, PETs, EKGs) give some representation of the scarring, but you can not translate that to what is going to happen over time,” says Natalia Trayanova, PhD, MS, of Johns Hopkins Department of Biomedical Engineering.
“With computational heart models, we create a dynamic digital image of the heart. We can then give the digital image an electrical stimulus and assess how the heart is able to respond. Then you can better predict what is going to happen.”
The computerized 3D models also mean better, more accurate treatment for heart conditions.
For example, a common treatment for a type of arrhythmia known as atrial fibrillation is ablation, or the burning of heart tissue. Ablation stops the erratic electrical impulses causing the arrhythmia, but it can also damage otherwise healthy heart cells. A personalized computational heart model for ablation intervention could allow doctors to more accurately see what areas should and shouldn’t be treated for a specific patient.
Using Deep Learning AI to Predict Health Outcomes
Trayanova’s colleague Dan Popescu is applying deep learning and AI technology to take the predictive capabilities of computerized heart models even further. In their recent paper in Nature Cardiovascular Research, the algorithm assessed health inputs for 269 patients and was able to predict the probability of a sudden cardiac event up to 10 years in advance.
“This is really the first time ever, as far as we know, where deep learning technology has been proven to analyze scarring of the heart in a successful way,” Popescu says.
Popescu and Trayanova say the AI algorithm gathers information from the 3D computational heart models with patient input data like MRIs, ethnicity, age, lifestyle, and clinical information. Together, this data can produce accurate and consistent estimates about probable survival times for patients who are at risk for sudden death.
“You can’t afford to be wrong. If you are wrong you can actually impact a patient’s quality of life dramatically,” Popescu says. “Having clinicians use this technology in the decision-making process will provide confidence in a better diagnosis and prognosis.”
While the current study was specific to patients with previous ischemic arrhythmias, Popescu says his algorithm can also be trained to assess other health conditions.
So when might you see this being used in mainstream healthcare? Trayanova predicts 3D imaging of heart models could be available in 2 years, but first it must undergo more clinical trials — some of which are underway right now.
Adding AI technology to the heart models will require additional FDA approval and studies, so the timeline is less clear. But perhaps the biggest hurdle is that, following approval, the technologies would need to be adopted and used by clinicians and caregivers.
“The much harder question to answer is, ‘When will doctors be perfectly comfortable with AI tools?’ And I don’t know the answer,” Popescu says. “How to use AI as an aid in the decision-making process is something that’s not currently taught in medical schools and residencies.”
Sources
Natalia Trayanovia, PhD, MS, professor of biomedical engineering and medicine, Johns Hopkins University
Dan Popescu, PhD, assistant research scientist, Department of Applied Mathematics and Statistics, Johns Hopkins University
Nature Cardiovascular Research. (2022). Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.https://doi.org/10.1038/s44161-022-00041-9
Natalie Sabin, MS, is a Mayo Clinic-trained wellness coach serving clients globally. She also contributes nutrition and health content to multiple publications.