Amnesty International Killer heart risk spots that most doctors cannot see

Linchpin is the system’s ability to analyze long cardiac imaging, as well as a full range of medical records, to detect in advance hidden information about the health of the patient’s heart.

The federal government, led by researchers at Johns Hopkins University, can save many lives and spare many unnecessary medical interventions, including implanting unnecessary inclins.

“We are currently patients who die in Prime their lives because they are not protected and others who collide with the fibrillation removal for the rest of their lives without any benefit,” said senior authors Natalia Tianova, a researcher who focused on using artificial intelligence in the heart. “We have the ability to predict very high accuracy, whether the patient is at risk of sudden cardiac death or not.”

The results were published today in The nature of cardiovascular research.

The hypertrophy of the hypertrophy is one of the most commonly inherited heart disease, affecting one in 200 to 500 individuals around the world, which is a major cause of sudden heart attack in youth and athletes.

Many patients with hypertrophy of the hypertrophy will live a normal life, but the percentage is very increasingly risk of sudden cardiac death. It was almost impossible for doctors to determine who these patients were.

The current clinical guidelines that doctors around the United States and Europe use to identify patients most at risk of fatal heart attacks about 50 % to determine the appropriate patients, “not much better than throwing blossoms.”

The team model greatly outperforms clinical instructions in all the demographics.

The multimedia AI predicts the ritual layer of the risk of arrhythmias (MAARS), the risk of sudden heart attack by analyzing a variety of medical data and records, and for the first time, explore all the information contained in the improved magnetic resonance images of contrast to the patient’s heart.

People who suffer from hypertrophy of the heart muscle suffer from fibrosis, or scars, through their heart, which are scars that increase the risk of sudden cardiac death. Although doctors were unable to understand the images of raw MRI, the artificial intelligence model has begun in the patterns of critical scars.

“People have not used deep learning in those pictures,” said Trianova. “We are able to extract this hidden information in the images that are not usually calculated.”

The team tested the model against real patients who were treated with traditional clinical guidelines at Johns Hopkins Hospital and Sanger Hart and Vascular Institute in North Carolina.

Compared to the clinical guidelines that were accurate in about half the time, the artificial intelligence model was 89 % accurate in all patients, decisively, 93 % accurate for people between 40 to 60 years old, and the population among the patients of huge myocardial disease at risk.

The artificial intelligence model can also describe the cause of the risk of developing patients so that doctors can design a medical plan to suit their own needs.

“Our study shows that the artificial intelligence model greatly enhances our ability to predict the highest risk compared to our current algorithms and thus has the power to convert clinical care.”

In 2022, the trayanova team created a different model of multimedia intelligence that provides a survival assessment for survival for patients with Infarcts, predicting whether someone will die from a heart attack and when.

The team plans to increase the new model test on more patients and expand the new algorithm for use with other types of heart disease, including heart hearts and right ventricular heart muscle.

Among the authors Changxin Lai, Minglang Yin, Eugene G. Kholmovski, Dan M. Popescu, Edem Binka, Stefan L. Zimmerman, Allison G. Hays, All of Johns Hopkins; Dai-Y Luand M. Roselle Abrahamof, the center of hypertrophy, at the University of California, San Francisco; Erika Sherrand Dermot M. Villanov atrium Health.