How can AI be used to predict cardiovascular events?
Sundar Pichai has recently introduced an extraordinary advancement in the realm of healthcare technology, which harnesses the power of artificial intelligence (AI) and utilizes eye scans to accurately forecast cardiovascular events.
This groundbreaking innovation has the potential to revolutionize the field of medical diagnostics by potentially rendering the need for traditional imaging techniques such as CT scans, MRIs, and X-rays. Instead, healthcare professionals will be able to acquire a detailed and holistic understanding of a patient’s internal condition through this cutting-edge AI-driven approach.
Pichai has placed significant emphasis on the transformative impact of AI in enhancing and redefining the quality of patient care, reinforcing the immense possibilities that lie ahead in the realm of healthcare innovation.
This groundbreaking development not only showcases the remarkable potential of AI, but also signifies a leap forward in improving medical outcomes and fostering a more efficient and precise healthcare system.
It’s widely known that the eye retina can provide valuable insight into overall health, so it’s no wonder that the team consisting of Google and Verily’s scientists decided to focus on this organ in particular. For the purpose of developing this algorithm, they analyzed a medical dataset containing eye scans and general medical data from nearly 300,000 patients.
Subsequently, neural networks were employed to extract valuable insights from this data, effectively analyzing intricate patterns and acquiring the ability to establish correlations between distinctive indicators observed in the eye scans and crucial metrics required for forecasting cardiovascular risk, including factors like age and blood pressure.
Not only that, but the algorithm is able to identify a person’s gender, smoking status, and predict the risk of a heart attack within five years using retinal imagery.
More precisely, it can help in the early detection of:
- Multiple sclerosis;
As we’ve mentioned in one of our previous articles, neural networks have an endless potential to be examined and implemented in healthcare in order to detect sophisticated signs that not even trained experts could do.
Moreover, the most important thing is that this technology could be easily accessible, cost-effective and available even in undeveloped areas since the only thing you need is a smartphone, an inexpensive condensing lens, and a DIY retinal camera.
However, many comments online dismissed the announcement, claiming that the viral video is over three years old – which is true – but the thing is, their research started over five years ago and they’re just seeing the first results now.
From what we know so far, the algorithm is waiting for a “green light” to operate independently soon, changing the landscape of eye disease detection and management.