Health & Medicine, UK (Commonwealth Union) – Artificial intelligence (AI) plays a significant role in various aspects of healthcare, revolutionizing the industry and improving patient outcomes. AI algorithms can analyze medical images such as X-rays, CT scans, and MRIs, assisting radiologists in detecting and diagnosing diseases like cancer, cardiovascular conditions, and neurological disorders. AI-powered image analysis can help improve accuracy, speed up diagnosis, and aid in early detection. The possibilities of AI in analyzing patterns, of biomarkers are predicted to play a key role in a variety of conditions with the potential to better diagnose and treat many conditions with enhanced capabilities.
Researchers from the University College London (UCL) have marked 5 subtypes of heart failure that may possibly play a role in forecasting the risks in the future.
Heart failure occurs when the heart is unable to pump sufficient blood to meet the requirements of the body. It occurs when the heart’s ability to contract and relax properly is compromised, leading to a decrease in its pumping efficiency.
In a healthy heart, blood flows from the lungs into the left atrium, then to the left ventricle, and finally gets pumped out to the rest of the body through the arteries. In heart failure, the heart muscle weakens or becomes stiff, affecting its pumping ability. This can result in several problems
For the findings that appeared in Lancet Digital Health, researchers explored detailed anonymized patient data from over 300,000 individuals at thirty years or older who had a diagnosis of heart failure in the UK over a span of twenty years.
With the application of many machine learning techniques, 5 subtypes were marked: early onset, late onset, atrial fibrillation related (atrial fibrillation leads to irregular heart rhythm), metabolic (associated with obesity however with a lesser rate of cardiovascular disease), as well as cardiometabolic (associated with obesity as well as cardiovascular disease).
The researchers observed variations between the subtypes in patients’ risk of dying in the year post-diagnosis. The all-cause mortality risks for 1 year were: early onset (20 percent), late-onset (46 percent), atrial fibrillation linked (61 percent), metabolic (11 percent), along with cardiometabolic (37 percent).
The study team created an app as well that clinicians could possibly utilize to find out which subtype an individual with heart failure has, which may possibly enhance forecasting of future risk as well as informal discussions with patients.
The Lead author Professor Amitava Banerjee of the UCL Institute of Health Informatics indicated that they sought to enhance the way they classify heart failure, with the goal of improving the understanding of the possible course of disease and letting patients know this. Right now, the way the disease progresses is difficult to forecast for individual patients. Certain individuals will remain stable for many years, while others may worsen quicker.
He further indicated that improved distinctions between the kinds of heart failure may also bring about more targeted treatments and could assist in thinking in a different way regarding possible therapies.
“The next step is to see if this way of classifying heart failure can make a practical difference to patients – whether it improves predictions of risk and the quality of information clinicians provide, and whether it changes patients’ treatment. We also need to know if it would be cost-effective. The app we have designed needs to be evaluated in a clinical trial or further research, but could help in routine care.”
In refraining from bias for a single machine learning technique, the researchers utilized 4 separate methods for grouping cases of heart failure. They used these techniques for data from 2 large UK primary care datasets, that represented the UK population as a whole and were associated with hospital admissions and death records as well.
