Dr. Craig Basman discusses new life-saving technology and the variables that can predict sudden cardiac events. Half of those who suffer cardiac arrest experience a telling symptom 24 hours before the incident, according to a study recently published in The Lancet Digital Health journal. This warning symptom was different in men and in women, researchers from Smidt Heart Institute found; the institute is located in the Cedars Sinai Medical Center in Los Angeles. For women, shortness of breath was the symptom that preceded an impending cardiac arrest, while for men, chest pain was the prominent complaint. SKIPPING THE SALT CAN REDUCE HEART DISEASE RISK BY ALMOST 20%, STUDY FINDS: 'KNOW WHAT YOU ARE CONSUMING' Sweating and seizure-like activity occurred in smaller subgroups of both genders, the researchers noted.

Diagnosing cardiac pathologies from echocardiograms correctly can be an extremely challenging endeavor that only very skilled cardiologists can perform with ease. This latest breakthrough contains the potential to completely shift the narrative when it comes to diagnostic medicine, and can ultimately save countless lives in the near future. Previously, researchers at the Smidt Heart Institute and Stanford University developed one of the first artificial intelligence technologies to assess cardiac function, specifically, left ventricular ejection fraction--the key heart measurement used in diagnosing cardiac function. Their research was published in the prestigious journal Nature. Building on this past research, the most recent study assessed the impact of artificial intelligence in clinical deployment as part of a prospective, blinded and randomized controlled clinical trial.

"These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories. When applied to these clinical images, the algorithm identified specific features - related to the thickness of heart walls and the size of heart chambers - to efficiently flag certain patients as suspicious for having the potentially unrecognized cardiac diseases. "The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert," said Ouyang.

Physician-scientists in the Smidt Heart Institute at Cedars-Sinai have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The new findings were published in JAMA Cardiology. "These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories.

Scientists from the Smidt Heart Institute at Cedars-Sinai have developed an artificial intelligence (AI) tool that can identify and distinguish between two life-threatening heart conditions that are often easy to miss--hypertrophic cardiomyopathy and cardiac amyloidosis. Their findings are published in JAMA Cardiology in a paper titled, "High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning." "Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis," the researchers wrote. "These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," explained David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis."