Researchers at Johns Hopkins University developed artificial intelligence technology that may be able to assess a patient's risk of sudden cardiac death, which is when the heart abruptly stops beating. Sometimes, modern medicine isn't enough to help keep us healthy. The Johns Hopkins University researchers said artificial intelligence can help accurately predict if and when someone's heart will stop beating years in advance. "It uses deep learning on images in combination with deep learning also on clinical data to predict the patient's risk of sudden cardiac death over a period of 10 years," said Dr. Natalia Trayanova, a professor of biomedical engineering and medicine. Trayanova's team developed the AI technology and published their work in a medical journal.

A new cardiac arrest-detecting AI has been revealed. If this health technology is proven effective, it can reduce death cases caused by sudden heart dysfunction. Natalia Trayanova, the senior author of the latest study, explained that more than 20% of the deaths across the world are caused by cardiac arrest (cardiac arrhythmias). Because of this, they decided to create a new artificial intelligence that can detect if an individual is about to have heart failure. Now, will this new cardia arrest-detecting AI help reduce cardiac arrest deaths?

Trayanova's lab built its AI to close the big prognostic gaps in SCD. To construct the system, her team trained a machine on 10 years of patient records from 156 people with heart conditions who agreed to share their medical information. They shared everything in their patient records, from MRIs of their hearts to 22 other pieces of potentially relevant information including race, weight, drug use, and hypertension. By feeding all the MRI scans into a machine learning system, researchers were able to discern hidden patterns, such as how scar tissue and other components of someone's heart make them predisposed to SCD. (Technically, a second AI was built to understand how smoking or other factors can impact this probability, too.) Then, after building their software, researchers validated their tool against patient data from 60 health centers across the U.S. The AI outperformed doctors in its diagnoses.

You are free to share this article under the Attribution 4.0 International license. A new artificial intelligence-based approach can predict if and when a patient could die of a heart attack. The technology, built on raw images of patient's diseased hearts and patient backgrounds, significantly improves on doctor's predictions and stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. "Sudden cardiac death caused by arrhythmia accounts for as many as 20% of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," says senior author Natalia Trayanova, a professor of biomedical engineering and medicine at Johns Hopkins University. "There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren't getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done."

A new artificial-intelligence-based approach can predict if and when a patient could die of cardiac arrest, a recent study led by researchers at John Hopkins University has found. The technology, built on raw images of patients' diseased hearts and patient backgrounds, stands to revolutionize clinical decision-making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. The new study was published in the journal, 'Nature Cardiovascular Research'. "Sudden cardiac death caused by arrhythmia accounts for as many as 20 per cent of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," said senior author Natalia Trayanova, the Murray B. Sachs Professor of Biomedical Engineering and Medicine. "There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren't getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done," she added.

A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patient's diseased hearts and patient backgrounds, stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. The work, led by Johns Hopkins University researchers, is detailed on April 7, 2022, in Nature Cardiovascular Research. "Sudden cardiac death caused by arrhythmia accounts for as many as 20 percent of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and Medicine. "There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren't getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done."

It detected high risk in the heart circled in red. A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patient's diseased hearts and patient backgrounds, stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. The work, led by Johns Hopkins University researchers, is detailed today in Nature Cardiovascular Research. "Sudden cardiac death caused by arrhythmia accounts for as many as 20 percent of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and Medicine.

A new artificial intelligence-based approach being led by John Hopkins University researchers claims it can predict if and when a patient could die of cardiac arrest. The technology, built on raw images of patient's diseased hearts and patient backgrounds, significantly improves on doctor's predictions and stands to revolutionise clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine's deadliest and most puzzling conditions. "Sudden cardiac death caused by arrhythmia accounts for as many as 20% of all deaths worldwide and we know little about why it's happening or how to tell who's at risk," said senior author Natalia Trayanova (pictured), a professor of biomedical engineering and medicine. "There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren't getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done."

Johns Hopkins researchers recently received a $195,000 Rapid Response Research grant from the National Science Foundation to, using machine learning, identify which COVID-19 patients are at risk of adverse cardiac events such as heart failure, sustained abnormal heartbeats, heart attacks, cardiogenic shock and death. Increasing evidence of COVID-19's negative impacts on the cardiovascular system highlights a great need for identifying COVID-19 patients at risk for heart problems, the researchers say. However, no such predictive capabilities currently exist. "This project will provide clinicians with early warning signs and ensure that resources are allocated to patients with the greatest need," says Natalia Trayanova, the Murray B. Sachs Professor in the Department of Biomedical Engineering at The Johns Hopkins University Schools of Engineering and Medicine and the project's principal investigator. The first phase of the one-year project, which just received IRB approval for Suburban Hospital and Sibley Memorial Hospital within the Johns Hopkins Health System (JHHS), will collect the following data from more than 300 COVID-19 patients admitted to JHHS: ECG, cardiac-specific laboratory tests, continuously-obtained vital signs like heart rate and oxygen saturation, and imaging data such as CT scans and echocardiography.