Cardiologists often struggle to assess heart attack risk. New startups using AI could help. For all the modern marvels of cardiology, we struggle to predict who will have a heart attack. Many people never get screened at all. Now, startups like Bunkerhill Health, Nanox.AI, and HeartLung Technologies are applying AI algorithms to screen millions of CT scans for early signs of heart disease. This technology could be a breakthrough for public health, applying an old tool to uncover patients whose high risk for a heart attack is hiding in plain sight.

But when deployed transparently, learned models also affect how users act in order to improve outcomes. The standard approach to learning predictive models is agnostic to induced user actions and provides no guarantees as to the effect of actions.

The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in individuals over 40. While the underlying mechanisms remain elusive, this study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility. Based on a unique dataset that combines demographic, biochemical, ECG, and thallium stress-tests, this study categorizes distinct subpopulations against varying risk profiles and then divides the population into 'at-risk' (AR) and 'not-at-risk' (NAR) groups using clustering algorithms. The study reveals strong association between the likelihood of experiencing a heart attack on the 13 risk factors studied. The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion that may be, further compromised by extraneous stress impacts, like anxiety and fear, aspects that have traditionally eluded data modeling predictions.

Investigators from Cedars-Sinai have created an artificial intelligence-enabled tool that may make it easier to predict if a person will have a heart attack. The tool, described in The Lancet Digital Health, accurately predicted which patients would experience a heart attack in five years based on the amount and composition of plaque in arteries that supply blood to the heart. Plaque buildup can cause arteries to narrow, which makes it difficult for blood to get to the heart, increasing the likelihood of a heart attack. A medical test called a coronary computed tomography angiography (CTA) takes 3D images of the heart and arteries and can give doctors an estimate of how much a patient's arteries have narrowed. Until now, however, there has not been a simple, automated and rapid way to measure the plaque visible in the CTA images.

Artificial Intelligence (AI) has applications in not just business and gaming but even health and fitness. It can predict threats to your health with increasing accuracy – one such health risk is a heart attack. Around 7.4 million people are living with heart disease in the UK, according to the British Heart Foundation. This is why proper prediction of heart attacks is the need of the day. A new paradigm combines new and old technologies to properly predict heart attacks.

According to a study published in the journal Radiology, when machine learning is combined with common heart scan, it can predict heart attacks and other cardiac events better than traditional risk models. Observing the worldwide data, heart disease is the most common and leading cause of death in both men and women, especially in the United States. Consequently, precision in risk assessment is mandatory for early intervention to say diet, exercise, drugs including cholesterol-lowering statins. In this context, CCTA (Coronary Computed Tomography Arteriography) provides with a highly detailed set of images of the heart vessels and happens to be a refining risk assessment tool. The study lead author Kevin M. Johnson, M.D., CCTA recently investigated a machine learning system which can mine the myriad details in CCTA-obtained images for a better and comprehensive prognostic picture.

Researchers from Google and sibling company Verily Life Sciences have developed a new algorithm using artificial intelligence to predict the risk of heart attack, stroke and other major cardiovascular events. Scientists studied data from 284,335 patients and found the "deep-learning" AI algorithm could identify risk factors based on age, blood pressure, gender, smoking status and others just by scanning the individuals' retinas. "The rear interior wall of the eye (the fundus) is chock-full of blood vessels that reflect the body's overall health," the Verge reported. "By studying their appearance with camera and microscope, doctors can infer things like an individual's blood pressure, age, and whether or not they smoke, which are all important predictors of cardiovascular health." Google's AI was able to differentiate patients who suffered a major cardiac event in the following five years and those who didn't with a 70 percent accuracy.

By looking at the human eye, Google's algorithms were able to predict whether someone had high blood pressure or was at risk of a heart attack or stroke, Google researchers said Monday, opening a new opportunity for artificial intelligence in the vast and lucrative global health industry. The algorithms didn't outperform existing medical approaches such as blood tests, according to a study of the finding published in the journal Nature Biomedical Engineering. The work needs to be validated and repeated on more people before it gains broader acceptance, several outside physicians said. But the new approach could build on doctors' current abilities by providing a tool that people could one day use to quickly and easily screen themselves for health risks that can contribute to heart disease, the leading cause of death worldwide. "This may be a rapid way for people to screen for risk," Harlan Krumholz, a cardiologist at Yale University who was not involved in the study, wrote in an email.