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Researchers Develop Artificial Intelligence Tool in Chest X-Rays to Predict Long Term Mortality

Each image was paired with a key piece of data: Did the person die over a 12-year period?

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Medical imagery can then be combined with AI to enable the reach of treatment to more people as well as provide targeted therapy based on individual symptoms. Pixabay

Researchers have developed an Artificial Intelligence (AI)-powered tool that can harvest information in chest X-rays to predict long-term mortality.

The findings of this study, published in the journal JAMA Network Open, could help to identify patients most likely to benefit from screening and preventive medicine for heart disease, lung cancer and other conditions.

“This is a new way to extract prognostic information from everyday diagnostic tests,” said one of the researchers, Michael Lu, from Massachusetts General Hospital (MGH) of Harvard Medical School. “It’s information that’s already there that we’re not using, that could improve people’s health,” Lu said. Lu and his colleagues developed a convolutional neural network – an AI tool for analysing visual information – called CXR-risk.

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Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. Pixabay

It was trained by having the network analyse more than 85,000 chest X-rays from 42,000 participants who took part in an earlier clinical trial. Each image was paired with a key piece of data: Did the person die over a 12-year period? The goal was for CXR-risk to learn the features or combinations of features on a chest X-ray image that best predict health and mortality.

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Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. They found that 53 per cent of people the neural network identified as “very high risk” died over 12 years, compared to fewer than four per cent of those that CXR-risk labeled as “very low risk.”

The study found that CXR-risk provided information that predicts long-term mortality, independent of radiologists’ readings of the x-rays and other factors, such as age and smoking status. Lu believes this new tool will be even more accurate when combined with other risk factors, such as genetics and smoking status. (IANS)

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Artificial Intelligence (AI) can Detect Signs of Irregular Heart Rhythm

The research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening

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The AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options. Pixabay

Artificial Intelligence (AI) can detect the signs of an irregular heart rhythm — atrial fibrillation (AF) — in an electrocardiogram (EKG), even if the heart is in normal rhythm at the time of a test, says new Mayo Clinic research.

The AI-enabled EKG can detect recent atrial fibrillation that occurred without symptoms or that is impending, potentially improving treatment options.

The research could improve the efficiency of the EKG, a noninvasive and widely available method of heart disease screening, said the study published in The Lancet.

While common, atrial fibrillation often is fleeting and is challenging to diagnose.

Artificial Intelligence, Heart, Rhythm
Artificial Intelligence (AI) can detect the signs of an irregular heart rhythm — atrial fibrillation (AF) — in an electrocardiogram (EKG), even if the heart is in normal rhythm at the time of a test, says new Mayo Clinic research. Pixabay

“When people come in with a stroke, we really want to know if they had atrial fibrillation in the days before the stroke, because it guides the treatment,” said Paul Friedman, Chair of the Department of Cardiovascular Medicine at Mayo Clinic.

Blood thinners are very effective for preventing another stroke in people with atrial fibrillation.

“For those without atrial fibrillation, using blood thinners increases the risk of bleeding without substantial benefit. That’s important knowledge. We want to know if a patient has AF,” said Friedman.

Researchers tested AI on normal-rhythm EKGs from a group of 36,280 patients, of whom 3,051 were known to have atrial fibrillation. The AI-enabled EKG correctly identified the subtle patterns of atrial fibrillation with 90 per cent accuracy.

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If proven out, AI-guided EKGs could direct the right treatment for disease caused by atrial fibrillation, even without symptoms.

Moreover, this technology can be processed using a smartphone or watch, making it readily available on a large scale. (IANS)