A score derived from machine learning that included information from stress cardiac magnetic resonance effectively predicted 10-year all-cause …

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.

A new machine learning system is better at predicting the likelihood of patients with cardiovascular problems dying within ten years than healthcare professionals' methods, according to a study presented at the EuroEcho 2021, a scientific meeting of the European Society Cardiology. Unlike traditional methods based solely on clinical data, the new machine learning system also includes results from imaging scans on the heart, measured by stress cardiovascular magnetic resonance (CMR). During this exam, patients receive a drug that mimics the effect of exercise on the heart and then undergo imaging using a magnetic resonance imaging scanner. Assessing the risk of death is commonly done in these patients. Usually, doctors use a limited amount of clinical information, including age, sex, smoking, blood pressure, and cholesterol levels.