An artificial intelligence platform developed by researchers at McGill University can accurately predict acute systemic inflammation even before the first symptoms appear, based on data provided by various wearable technologies.
This could one day allow doctors to tackle the problem days earlier, particularly in patients whose health is already fragile and for whom a new infection could have serious consequences.
This technology could also potentially reduce costs for the healthcare system by avoiding complications and hospitalizations.
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“We were very interested to see if physiological data measured using wearable sensors (…) could be used to train an artificial intelligence system capable of detecting infection or disease resulting from inflammation,” explained the study’s lead author, Professor Dennis Jensen of McGill University’s Department of Kinesiology and Physical Education, who discussed his work firsthand with The Canadian Press.
“We wondered if we could detect early changes in physiology, and from there predict that someone is about to get sick.”
The artificial intelligence model developed by Professor Jensen and his colleagues uses biometric data generated by a smart ring, smart watch, or smart clothing to accurately predict acute systemic inflammation—an early immune response to viral respiratory tract infections.
Although it is a natural defence mechanism of the body that usually does not resolve itself, this inflammation can cause serious health problems, especially in populations with pre-existing health conditions.
“It’s like an iceberg,” illustrated Professor Jensen, who assures that this model is the only one in the world to use such physiological measures, and not symptoms, to detect a problem. “Once the ice cracks on the surface, the symptoms have started and it is a little too late to start treating.”
Experience
McGill researchers administered an attenuated influenza vaccine to 55 healthy adults to simulate infection. The subjects were monitored from seven days before inoculation to five days afterward.
Participants also wore, simultaneously and for the duration of the study, a connected ring, watch and clothing to continuously monitor several physiological parameters and activities, including heart rate, heart rate variability, body temperature, respiratory rate, blood pressure, physical activity and sleep quality.
The researchers also measured biomarkers of systemic inflammation using repeated blood samples, performed PCR tests to detect the presence of respiratory pathogens, and used a mobile app to collect symptoms reported by participants, it was explained in a press release.
In total, more than two billion data points were collected to train machine learning algorithms. Ten different AI models were developed, but the researchers ultimately decided to retain only the model that used the least amount of data for the rest of the project.
This model correctly detected nearly 90 per cent of actual positive cases and was deemed more practical for daily monitoring.
Individually, Professor Jensen said, none of the physiological or activity measures coming from just the ring, watch, or T-shirt are sensitive enough to detect how the body is responding.
“An increase in heart rate alone might only be two beats per minute, and that’s not really clinically relevant,” he explained. “The decrease in heart rate variability can be very modest. The increase in temperature can be very modest. So the idea was that by looking at multimodal measures or several different measures, we would be able to identify subtle changes in physiology.”
Remarkably, the algorithms also successfully detected systemic inflammation in four participants infected with SARS-CoV-2 during the study. In each case, the algorithms reported the immune response up to 72 hours before the onset of symptoms or confirmation of infection by PCR testing.
Ultimately, the researchers hope to develop a system that will inform the patient of possible inflammation so they can communicate with their healthcare provider.
“In medicine we say you have to provide the right treatment to the right person at the right time,” said Professor Jensen.
It is therefore crucial to tackle the problem as early as possible, whether it is a simple cold or cancer, “because once the symptoms appear, it starts to get late,” he said.
By expanding the therapeutic window within which intervention can be performed, he added, lives could be saved and significant cost savings could be achieved by avoiding hospitalizations and enabling the management of chronic problems or even aging at home.
“In a way, we hope to revolutionize personalized medicine,” concluded Professor Jensen.
The findings of this study were published in the journal The Lancet Digital Health.
–This report by La Presse Canadienne was translated by CityNews
