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Mount Sinai researchers use computer algorithms to diagnose HCM from echos

November 22, 2016

Computer algorithms can automatically interpret echocardiographic images and distinguish between pathological hypertrophic cardiomyopathy (HCM) and physiological changes in athletes' hearts, according to research from the Icahn School of Medicine at Mount Sinai (ISMMS), published online yesterday in the Journal of the American College of Cardiology.

HCM is a disease in which a portion of the myocardium enlarges, creating functional impairment of the heart. It is the leading cause of sudden death in young athletes. Diagnosing HCM is challenging since athletes can present with physiological hypertrophy, in which their hearts appear large, but do not feature the pathological abnormality of HCM. The current standard of care requires precise phenotyping of the two similar conditions by a highly trained cardiologist.

"Our research has demonstrated for the first time that machine-learning algorithms can assist in the discrimination of physiological versus pathological hypertrophic remodeling, thus enabling easier and more accurate diagnoses of HCM," said senior study author Partho P. Sengupta, MD, Director of Cardiac Ultrasound Research and Professor of Medicine in Cardiology at the Icahn School of Medicine at Mount Sinai. "This is a major milestone for echocardiography, and represents a critical step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images. This could help novice echo readers with limited experience, making the diagnosis rapid and more widely available."

Using data from an existing cohort of 139 male subjects who underwent echocardiographic imaging at ISMMS (77 verified athlete cases and 62 verified HCM cases), the researchers analyzed the images with tissue tracking software and identified variable sets to incorporate in the machine-learning models. They then developed a collective machine-learning model with three different algorithms to differentiate the two conditions. The model demonstrated superior diagnostic ability comparable to conventional 2D echocardiographic and Doppler-derived parameters used in clinical practice.

"Our approach shows a promising trend in using automated algorithms as precision medicine techniques to augment physician-guided diagnosis," said study author Joel Dudley, PhD, Director of the Institute for Next Generation Healthcare and Director of the Center for Biomedical Informatics at ISMMS. "This demonstrates how machine-learning models and other smart interpretation systems could help to efficiently analyze and process large volumes of cardiac ultrasound data, and with the growth of telemedicine, it could enable cardiac diagnoses even in the most resource-burdened areas."
-end-
The team included researchers from both Dr. Sengupta's and Dr. Dudley's labs, including medical student Sukrit Narula, Khader Shameer, PhD, and Alaa Mabrouk Salem Omar, MD, PhD. The team is now in the process of developing other artificial intelligence-powered cardiovascular phenotyping algorithms to deploy to help clinicians, echocardiography technicians, and medical students to make diagnoses.

About the Mount Sinai Health System

The Mount Sinai Health System is an integrated health system committed to providing distinguished care, conducting transformative research, and advancing biomedical education. Structured around seven hospital campuses and a single medical school, the Health System has an extensive ambulatory network and a range of inpatient and outpatient services--from community-based facilities to tertiary and quaternary care.

The System includes approximately 7,100 primary and specialty care physicians; 12 joint-venture ambulatory surgery centers; more than 140 ambulatory practices throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and 31 affiliated community health centers. Physicians are affiliated with the renowned Icahn School of Medicine at Mount Sinai, which is ranked among the highest in the nation in National Institutes of Health funding per investigator. The Mount Sinai Hospital is in the "Honor Roll" of best hospitals in America, ranked No. 15 nationally in the 2016-2017 "Best Hospitals" issue of U.S. News & World Report. The Mount Sinai Hospital is also ranked as one of the nation's top 20 hospitals in Geriatrics, Gastroenterology/GI Surgery, Cardiology/Heart Surgery, Diabetes/Endocrinology, Nephrology, Neurology/Neurosurgery, and Ear, Nose & Throat, and is in the top 50 in four other specialties. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 10 nationally for Ophthalmology, while Mount Sinai Beth Israel, Mount Sinai St. Luke's, and Mount Sinai West are ranked regionally. Mount Sinai's Kravis Children's Hospital is ranked in seven out of ten pediatric specialties by U.S. News & World Report in "Best Children's Hospitals."

For more information, visit http://www.mountsinai.org/, or find Mount Sinai on Facebook, Twitter and YouTube.

The Mount Sinai Hospital / Mount Sinai School of Medicine

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Mount Sinai researchers use computer algorithms to diagnose HCM from echos
Computer algorithms can automatically interpret echocardiographic images and distinguish between pathological hypertrophic cardiomyopathy (HCM) and physiological changes in athletes' hearts, according to research from the Icahn School of Medicine at Mount Sinai
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