New AI Tool Can Tell Which CO.VID-19 Patient Will Develop Severe Lung Disease

On Monday, researchers in China and the US said that they have developed an AI tool that can predict which new CO.VID-19 patient will develop severe lung disease.

When the algorithm is deployed, it will help doctors with choosing patients to prioritize care in healthcare systems with limited resources, according to the study’s co-author, physician and professor Megan Coffee from the Grossman School of Medicine of New York University.

The tool was able to discover various surprising indicators that would suggest who likely went on to develop ARDS (acute respiratory disease syndrome), which is basically CO.VID-19 that fills the patient’s lungs with fluid, killing about 50% of patients infected with the disease.

A machine-learning algorithm was applied to the data of 53 CO.VID-19 patients at two hospitals in China. From this, the research team found that the changes in three indicators including hemoglobin levels, reported body aches, and levels of liver enzyme ALT (alanine aminotransferase), help make the best prediction about the disease.

With this information and other factors, the tool could predict a patient’s risk of ARDS with as high as 80% accuracy.

On the other hand, characteristics considered CO.VID-19 hallmarks, such as ground-glass opacity in the lungs, fever, as well as strong immune response, did not help much in predicting which patient would get ARDS. Sex and age were not helpful either though studies found that men 60 years and older are at a higher risk.

Coffee also pointed out an interesting fact that the data points used by the machine and clinicians are different.

This is not the first time AI has kicked in to help doctors, for example, there is already an AI tool that can predict which patient will get skin cancer.

However, what makes this case special is that doctors and researchers are learning about CO.VID-19 on the fly, and the tool can help them head the right way. Hence, they will make better decisions with critical patients.

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