Preparticipation cardiovascular examination (PPCE) aims to prevent sudden cardiac death (SCD) by identifying athletes with structural or electrical cardiac abnormalities. Anthropometric measurements, such as waist circumference, limb lengths, and torso proportions to detect Marfan syndrome, can indicate elevated cardiovascular risk. Traditional manual methods are labor-intensive, operator-dependent, and challenging to scale. We present a fully automated deep-learning approach to estimate five key anthropometric measurements from 2D synthetic human body images. Using a dataset of 100,000 images derived from 3D body meshes, we trained and evaluated VGG19, ResNet50, and DenseNet121 with fully connected layers for regression. All models achieved sub-centimeter accuracy, with ResNet50 performing best, achieving a mean MAE of 0.668 cm across all measurements. Our results demonstrate that deep learning can deliver accurate anthropometric data at scale, offering a practical tool to complement athlete screening protocols. Future work will validate the models on real-world images to extend applicability.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on "Special Report." A new study found that artificial intelligence could be used to help detect risk signs and possibly even prevent sudden cardiac death. "When the data is fulsome and accurate and has a large enough sample size, AI will be able to identify patterns and correlations that humans might struggle to see, especially when they require two or more factors or have seemingly contrarian conclusions," Phil Siegel, the founder of the Center for Advanced Preparedness and Threat Response Simulation, told Fox News Digital. Siegel's comments come after the results of preliminary research by the American Health Association found that AI was able to identify people who were at more than a 90% risk of sudden death, according to a report on the study in Medical Xpress. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

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.

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."

A novel artificial intelligence score provides a more accurate forecast of the likelihood of patients with suspected or known coronary artery disease dying within 10 years than established scores used by health professionals worldwide. The research is presented today at EuroEcho 2021, a scientific congress of the European Society of Cardiology (ESC).[1] Unlike traditional methods based on clinical data, the new score also includes imaging information on the heart, measured by stress cardiovascular magnetic resonance (CMR). "Stress" refers to the fact that patients are given a drug to mimic the effect of exercise on the heart while in the magnetic resonance imaging scanner. "This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death," said study author Dr. Theo Pezel of the Johns Hopkins Hospital, Baltimore, US. "The findings indicate that patients with chest pain, dyspnoea, or risk factors for cardiovascular disease should undergo a stress CMR exam and have their score calculated. This would enable us to provide more intense follow-up and advice on exercise, diet, and so on to those in greatest need."

A novel artificial intelligence score provides a more accurate forecast of the likelihood of patients with suspected or known coronary artery disease dying within 10 years than established scores used by health professionals worldwide. The research is presented today at EuroEcho 2021, a scientific congress of the European Society of Cardiology (ESC). Unlike traditional methods based on clinical data, the new score also includes imaging information on the heart, measured by stress cardiovascular magnetic resonance (CMR). "Stress" refers to the fact that patients are given a drug to mimic the effect of exercise on the heart while in the magnetic resonance imaging scanner. This is the first study to show that machine learning with clinical parameters plus stress CMR can very accurately predict the risk of death.