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.”

The 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 potentially unrecognized cardiac diseases.

“The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert,” said Ouyang. “This is because the algorithm picks up subtle cues on ultrasound videos that distinguish between heart conditions that can often look very similar to more benign conditions, as well as to each other, on initial review.”

It can be challenging to distinguish between similar-appearing diseases without comprehensive testing. This algorithm distinguishes not only abnormal from normal, but also between which underlying potentially life-threatening cardiac conditions may be present—with warning signals that are now detectable well before the disease clinically progresses to the point where it can impact health outcomes.

“One of the most important aspects of this AI technology is not only the ability to distinguish abnormal from normal, but also to distinguish between these abnormal conditions, because the treatment and management of each cardiac disease is very different,” said Ouyang.

Researchers plan to soon launch clinical trials for patients flagged by the AI algorithm for suspected cardiac amyloidosis. Patients enrolled in the trial will be seen by experts in the cardiac amyloidosis program at the Smidt Heart Institute.

A clinical trial for patients flagged by the algorithm for suspected hypertrophic cardiomyopathy started at Cedars-Sinai.

“The use of artificial intelligence in cardiology has evolved rapidly and dramatically in a relatively short period of time,” added Susan Cheng, MD, MPH, director of the Institute for Research on Healthy Aging in the department of cardiology at the Smidt Heart Institute and co-senior author of the study. “These remarkable strides—which span research and clinical care—can make a tremendous impact in the lives of our patients.