Scientists Show How AI Could Predict Risk of Cardiovascular Disease

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In order to predict the risk of patient developing heart diseases, an international team of researchers from the University of Melbourne and University of Otago have developed a way to use Artificial Intelligence (AI) to predict the disease.

AI is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

According to the paper published in the journal Nature Biological Engineering, the researchers described using retinal blood vessel scans as a data-source for a deep learning system to teach it to recognise the signs of cardiovascular disease in people.

Scientists Show How AI Could Predict Risk of Cardiovascular Disease
Scientists Show How AI Could Predict Risk of Cardiovascular Disease

For over 100 years, doctors have peered into the eyes of patients looking for changes in retinal vasculature—blood vessels in the retina that can reflect the impact of high blood pressure over a period of time. Such an impact can be an indicator of impending cardiovascular disease.

Over time, medical scientists have developed instruments that allow eye doctors to get a better look at the parts of the eye most susceptible to damage from hypertension and have used them as a part of a process to diagnose patients that are likely to develop the disease. But such tools still require a medical professional to make the final call. In this new effort, the researchers have taught an AI system to recognise the same sorts of symptoms without the need for human intervention.

The work by the team involved training an AI system to spot markers for cardiovascular disease by studying retinal vasculature scans (looking for degree of retinal arteriolar narrowing, for example) along with data describing markers for a given patient. But for such a system to be able to do this work, it must first learn to recognise markers by studying thousands of images along with data highlighting associated markers for each patient. In this case, the researchers trained the system on over 70,000 such images from people with diverse backgrounds.

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To test the accuracy of the system, the researchers compared results from their AI system with diagnoses given by doctors who used traditional testing methods to diagnose cardiovascular disease risk, e.g., diabetes, obesity, BMI, cholesterol and glycated hemoglobin levels. The researchers claim their system outperformed the experts based on patient tracking after testing.

The researchers suggest that AI-based diagnostic systems for predicting onset of cardiovascular disease could provide a new tool for doctors looking to catch the disease earlier in patients, thereby lessening symptoms and hopefully reducing tragic outcomes.

The research revealed that using the semi-automated robot even by users who had no previous training was much more manageable than non-intelligent methods, such as ones that rely on using magnets outside the body to control how the robotic arm moves inside the body, or more traditional colonoscopy methods where experienced medical practitioners use conventional endoscopes and guide it through the colon.

“The inherent complexity of navigating magnetic endoscopes with a single external permanent magnet can be overcome by the developed intelligent control strategies. These were able to mask the unintuitive nature of interacting magnetic fields and field gradients,” the research said.

In addition to being used for early detection of colon cancer, the technology also has the potential to be used in other procedures such as pancreatic endoscopy, bronchoscopy, and gastroscopy, the researchers said.

 

 

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