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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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AI-based Algorithm to Help Doctors Treat Traumatic Brain Injury

AI-based algorithm to treat brain injury developed

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An AI-based algorithm will help doctors treat patients with severe traumatic brain injury (TBI). Pixabay

Researchers, including one of Indian-origin, have developed an artificial intelligence (AI) based algorithm that could help doctors treat patients with severe traumatic brain injury (TBI).

The algorithms can predict the probability of the patient dying within 30-days with an accuracy of 80-85 per cent, said the study published in the journal Scientific Reports.

“A dynamic prognostic model like this has not been presented before. Although this is a proof-of-concept and it will still take some time before we can implement algorithms like this into daily clinical practice, our study reflects how and into what direction modern intensive care is evolving”, said Indian-origin researcher and study author Rahul Raj from Helsinki University Hospital in the Finland.

Traumatic brain injury is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries.

The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies.

Brain Injury
Traumatic brain injury is a significant global cause of mortality and morbidity. Pixabay

This is why researchers at Helsinki University Hospital (HUS) started to develop an artificial intelligence (AI) based algorithm that could help doctors treat patients with severe TBI.

At its best, such an algorithm could predict the outcome of the individual patient and give objective data regarding the condition and prognosis of the patient and how it changes during treatment.

“We have developed two separate algorithms. The first algorithm is simpler and is based only upon objective monitor data. The second algorithm is slightly more complex and includes data regarding the level of consciousness, measured by the widely used Glasgow Coma Scale score,” said study researcher Eetu Pursiainen.

As expected, the accuracy of the more complex algorithm is slightly better than for the simpler algorithm.

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“Still, the accuracy of both algorithms is surprisingly good, considering that the simpler model is based upon only three main variables and the more complex upon five main variables”, Pursiainen said.

In the future, the algorithms still have to be validated in national and international external datasets, the researchers concluded. (IANS)