Deaths from COVID-19 may have caught more attention lately, but heart disease remains the leading cause of death in the United States.
More than 300,000 Americans will die this year of sudden cardiac arrest (also called sudden cardiac death, or SCD), when the heart abruptly stops working.
These events happen 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, Baltimore.
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There, researchers are working to create more accurate and personalized models of the heart – and not just any heart, your heart, if you have heart disease.
“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. “With this new technology, you can have much more nuanced predictions of probability of an event over time.”
Put another way: With AI, clinicians may be able not only to predict if someone is at risk for sudden cardiac arrest, but also when it is most likely to happen. They can do this using 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, electrical impulses flow from the top to the bottom of the organ. Healthy heart cells relay this electricity seamlessly. But in a heart damaged by inflammation or a past heart attack, scar tissue will block 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 someone’s danger of sudden cardiac death.
Seeing the heart in 3D
Today’s tests offer some insights into the heart’s makeup. For example, MRI scans can reveal damaged areas. PET scans can show inflammation. And 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. They can’t predict the future. That’s 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 are 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 cannot translate that to what is going to happen over time,” says Natalia Trayanova, PhD, of the 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 burning some 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 could allow doctors to see more accurately what areas should and shouldn’t be treated for a specific patient.