TELANGANA, INDIA - From exaggerated medical dramas to every health-related Google search, heart failure is a constant. It is the most common cause of death among men and women worldwide. Cardiovascular diseases account for 30 percent of all fatalities in the United States, 45 percent in Europe, and 35 percent in Asia. To combat such a rapidly deteriorating health condition, artificial intelligence (AI) approaches like machine learning (ML) and deep learning models are being employed to advance cardiovascular care.
Despite modern-day diagnostics, early detection and diagnosis remain a challenge for cardiologists, primary care doctors, and healthcare providers. Some also criticize procedures for interpreting a patient’s medical history and tests along the lines of a physician’s clinical experience and the subjective understanding of the underlying human heart function, which is often error-prone and inefficient.1
One out of four deaths in India is from cardiovascular disease, which involves a stroke or heart disease in over 80 percent of cases. The urgent need for accurate, quick, and automated medical procedures to improve healthcare, all while simultaneously reducing the costs involved, has accelerated research on AI applications in cardiology in the country.
Apollo Hospitals, a leading chain of hospitals and medical education institutes in ‘presence-across-nation India,’ recently released a report that highlights the key causes of heart disease and came up with an aggressive solution-led approach.2 The report highlighted factors such as chronic stress, high-calorie diets, and low to negligible physical activity, all of which fuel a trend toward high blood pressure among urban Indians. This phenomenon is not just confined to India. Such lifestyles are prevalent across the world and maThe content herein is subject to copyright by The Yuan. All rights reserved. The content of the services is owned or licensed to The Yuan. Such content from The Yuan may be shared and reprinted but must clearly identify The Yuan as its original source. Content from a third-party copyright holder identified in the copyright notice contained in such third party’s content appearing in The Yuan must likewise be clearly labeled as such.