Heart Attack Prediction by Heart-monitoring Tools

Author : P. D.GUPTA

(Former Director Grade Scientist, Centre for Cellular and Molecular Biology, Hyderabad, India.)


Our past president Dr. APG Kalam sahib had massive heart attack while he was delivering lecture. Think about you are travelling in a car and your driver has sudden heart attack. To avoid such incidences, Scientists are working tirelessly to find ways to predict heart attacks years before any symptoms arise — because early prediction means early intervention, and early intervention can save lives. Traditionally, Physicians have relied on standard assessments of cholesterol, blood pressure, lifestyle factors and health conditions such as diabetes to predict whether a patient is likely to suffer a heart attack. 

Age, lipid levels, obesity, lack of activity and stress can all contribute to blocked arteries, preventing blood flow to the heart.  By gathering data, test results and patient information, cardiologists can generate a score that indicates a patient’s heart attack risk.“It’s not an exact prediction,” says Quyyumi. “We use the scores to reduce risk and to prevent disease, heart attack or sudden cardiac death. 

With the development of predictive capacities of electronic gazettes combined with artificial intelligence (AI) Apple Company produced heart-monitoring tools   to power a massive heart study.  Toyota is working with researchers at the University of Michigan to study how their cars can detect when drivers are having a cardiac event. Now the new astrologers (AI) have come in picture, these were able to determine an individual’s risk in a matter of minutes and even 10 years before hand.  

The Eyes Are the Window to the Heart  

Dr. Lily Peng, a product manager at Google, works with a team of researchers to learn how blood vessels in the eyes can predict heart attack risk. According to Peng, patients’ retinal (the screen at the back of the eye where all the images are formed when we see through our eyes) scans contribute valuable information to her group’s algorithm generated by AI. 

Together, the components were quite successful at recognizing which patients had experienced a cardiovascular event. “Given the retinal image of one patient who later experienced a major cardiovascular (CV) event, such as a heart attack, and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70 percent of the time,” says Peng. “This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.” Although testing is still in early phases, Peng recognizes this as a very promising start. (The author has his own study and views)