Latest news with #McGillUniversity
Global News
7 hours ago
- Health
- Global News
McGill University team develops AI that can detect infection before symptoms appear
Researchers at McGill University say they developed an artificial intelligence platform that can predict when someone is about to come down with a respiratory tract infection before they start to feel sick. In what researchers are calling a 'world first,' the study involved participants who wore a ring, a watch and a T-shirt, all of which were equipped with censors that recorded their biometric data. By analyzing the data, researchers were able to accurately predict acute systemic inflammation — an early sign of a respiratory infection such as COVID-19. Published in The Lancet Digital Health, the study says the AI platform can one day help doctors address health problems much earlier than they normally would, particularly in patients who are fragile and for whom a new infection could have serious consequences. It could also potentially reduce costs for the health-care system by preventing complications and hospitalizations. '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 an infection or disease resulting from inflammation,' explained the study's lead author, Prof. Dennis Jensen of McGill University's department of kinesiology and physical education. Story continues below advertisement 'We wondered if we could detect early changes in physiology and, from there, predict that someone is about to get sick.' Get daily National news Get the day's top news, political, economic, and current affairs headlines, delivered to your inbox once a day. Sign up for daily National newsletter Sign Up By providing your email address, you have read and agree to Global News' Terms and Conditions and Privacy Policy Jense says the AI model his team created is the first in the world to use physiological measures — including heart rate, heart rate variability, body temperature, respiratory rate, blood pressure — rather than symptoms, to detect a problem. Acute systemic inflammation is a natural defence mechanism of the body that usually resolves on its own, but it can cause serious health problems, especially in populations with pre-existing conditions. 'The whole idea is kind of like an iceberg,' Jensen said. 'Kind of when the ice cracks the surface, that's like when you're symptomatic, and then it's too late to really do much to treat it.' During the study, McGill researchers administered a weakened flu vaccine to 55 healthy adults to simulate infection in their bodies. The subjects were monitored seven days before inoculation and five days after. Participants wore a smart ring, smart watch, and a smart T-shirt simultaneously throughout the study. As well, researchers collected biomarkers of systemic inflammation using blood samples, PCR tests to detect the presence of respiratory pathogens, and a mobile app to collect symptoms reported by participants. In total, more than two billion data points were collected to train machine learning algorithms. Ten different AI models were developed, but the researchers chose the model that used the least amount of data for the remainder of the project. The chosen model correctly detected nearly 90 per cent of actual positive cases and was deemed more practical for daily monitoring. Story continues below advertisement On their own, Jensen said, none of the data collected from the ring, watch, or T-shirt alone is sensitive enough to detect how the body is responding. 'An increase in heart rate alone may only correspond to two beats per minute, which is 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 … several different measurements, we would be able to identify subtle changes in physiology.' The algorithms also successfully detected systemic inflammation in four participants infected with COVID-19 during the study. In each case, the algorithms flagged the immune response up to 72 hours before symptoms appeared or infection was confirmed by PCR testing. Ultimately, the researchers hope to develop a system that will inform patients of possible inflammation so they can contact their health-care provider. 'In medicine, we say that you have to give the right treatment to the right person at the right time,' Jensen said. By expanding the therapeutic window in which doctors can intervene, he added, they could save lives and achieve significant savings by avoiding hospitalizations and enabling home management of chronic conditions or even aging. 'In a way, we hope to revolutionize personalized medicine.' This report by The Canadian Press was first published July 30, 2025.
Vancouver Sun
9 hours ago
- Health
- Vancouver Sun
McGill researchers develop AI that predicts respiratory illness before symptoms show
Researchers at McGill University say they developed an artificial intelligence platform that can predict when someone is about to come down with a respiratory tract infection before they start to feel sick. In what researchers are calling a 'world first,' the study involved participants who wore a ring, a watch and a T-shirt, all of which were equipped with sensors that recorded their biometric data. By analyzing the data, researchers were able to accurately predict acute systemic inflammation — an early sign of a respiratory infection such as COVID-19. Published in The Lancet Digital Health, the study says the AI platform can one day help doctors address health problems much earlier than they normally would, particularly in patients who are fragile and for whom a new infection could have serious consequences. It could also potentially reduce costs for the health-care system by preventing complications and hospitalizations. Start your day with a roundup of B.C.-focused news and opinion. By signing up you consent to receive the above newsletter from Postmedia Network Inc. A welcome email is on its way. If you don't see it, please check your junk folder. The next issue of Sunrise will soon be in your inbox. Please try again Interested in more newsletters? Browse here. '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 an infection or disease resulting from inflammation,' explained the study's lead author, Prof. Dennis Jensen of McGill University's department of kinesiology and physical education. 'We wondered if we could detect early changes in physiology and, from there, predict that someone is about to get sick.' Jense says the AI model his team created is the first in the world to use physiological measures — including heart rate, heart rate variability, body temperature, respiratory rate, blood pressure _ rather than symptoms, to detect a problem. Acute systemic inflammation is a natural defence mechanism of the body that usually resolves on its own, but it can cause serious health problems, especially in populations with pre-existing conditions. 'The whole idea is kind of like an iceberg,' Jensen said. 'Kind of when the ice cracks the surface, that's like when you're symptomatic, and then it's too late to really do much to treat it.' During the study, McGill researchers administered a weakened flu vaccine to 55 healthy adults to simulate infection in their bodies. The subjects were monitored seven days before inoculation and five days after. Participants wore a smart ring, smart watch, and a smart T-shirt simultaneously throughout the study. As well, researchers collected biomarkers of systemic inflammation using blood samples, PCR tests to detect the presence of respiratory pathogens, and a mobile app to collect symptoms reported by participants. In total, more than two billion data points were collected to train machine learning algorithms. Ten different AI models were developed, but the researchers chose the model that used the least amount of data for the remainder of the project. The chosen model correctly detected nearly 90 per cent of actual positive cases and was deemed more practical for daily monitoring. On their own, Jensen said, none of the data collected from the ring, watch, or T-shirt alone is sensitive enough to detect how the body is responding. 'An increase in heart rate alone may only correspond to two beats per minute, which is 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 … several different measurements, we would be able to identify subtle changes in physiology.' The algorithms also successfully detected systemic inflammation in four participants infected with COVID-19 during the study. In each case, the algorithms flagged the immune response up to 72 hours before symptoms appeared or infection was confirmed by PCR testing. Ultimately, the researchers hope to develop a system that will inform patients of possible inflammation so they can contact their health-care provider. 'In medicine, we say that you have to give the right treatment to the right person at the right time,' Jensen said. By expanding the therapeutic window in which doctors can intervene, he added, they could save lives and achieve significant savings by avoiding hospitalizations and enabling home management of chronic conditions or even aging. 'In a way, we hope to revolutionize personalized medicine.' Our website is the place for the latest breaking news, exclusive scoops, longreads and provocative commentary. Please bookmark and sign up for our daily newsletter, Posted, here .
Edmonton Journal
9 hours ago
- Health
- Edmonton Journal
McGill researchers develop AI that predicts respiratory illness before symptoms show
Article content Researchers at McGill University say they developed an artificial intelligence platform that can predict when someone is about to come down with a respiratory tract infection before they start to feel sick. Article content In what researchers are calling a 'world first,' the study involved participants who wore a ring, a watch and a T-shirt, all of which were equipped with sensors that recorded their biometric data. By analyzing the data, researchers were able to accurately predict acute systemic inflammation — an early sign of a respiratory infection such as COVID-19. Article content Article content Article content Published in The Lancet Digital Health, the study says the AI platform can one day help doctors address health problems much earlier than they normally would, particularly in patients who are fragile and for whom a new infection could have serious consequences. It could also potentially reduce costs for the health-care system by preventing complications and hospitalizations. Article content Article content '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 an infection or disease resulting from inflammation,' explained the study's lead author, Prof. Dennis Jensen of McGill University's department of kinesiology and physical education. Article content Article content Jense says the AI model his team created is the first in the world to use physiological measures — including heart rate, heart rate variability, body temperature, respiratory rate, blood pressure _ rather than symptoms, to detect a problem. Article content Article content Acute systemic inflammation is a natural defence mechanism of the body that usually resolves on its own, but it can cause serious health problems, especially in populations with pre-existing conditions. Article content 'The whole idea is kind of like an iceberg,' Jensen said. 'Kind of when the ice cracks the surface, that's like when you're symptomatic, and then it's too late to really do much to treat it.' Article content During the study, McGill researchers administered a weakened flu vaccine to 55 healthy adults to simulate infection in their bodies. The subjects were monitored seven days before inoculation and five days after.
Winnipeg Free Press
10 hours ago
- Health
- Winnipeg Free Press
McGill University team develops AI that can detect infection before symptoms appear
MONTRÉAL – Researchers at McGill University say they developed an artificial intelligence platform that can predict when someone is about to come down with a respiratory tract infection before they start to feel sick. In what researchers are calling a 'world first,' the study involved participants who wore a ring, a watch and a T-shirt, all of which were equipped with censors that recorded their biometric data. By analyzing the data, researchers were able to accurately predict acute systemic inflammation — an early sign of a respiratory infection such as COVID-19. Published in The Lancet Digital Health, the study says the AI platform can one day help doctors address health problems much earlier than they normally would, particularly in patients who are fragile and for whom a new infection could have serious consequences. It could also potentially reduce costs for the health-care system by preventing complications and hospitalizations. '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 an infection or disease resulting from inflammation,' explained the study's lead author, Prof. Dennis Jensen of McGill University's department of kinesiology and physical education. 'We wondered if we could detect early changes in physiology and, from there, predict that someone is about to get sick.' Jense says the AI model his team created is the first in the world to use physiological measures — including heart rate, heart rate variability, body temperature, respiratory rate, blood pressure — rather than symptoms, to detect a problem. Acute systemic inflammation is a natural defence mechanism of the body that usually resolves on its own, but it can cause serious health problems, especially in populations with pre-existing conditions. 'The whole idea is kind of like an iceberg,' Jensen said. 'Kind of when the ice cracks the surface, that's like when you're symptomatic, and then it's too late to really do much to treat it.' During the study, McGill researchers administered a weakened flu vaccine to 55 healthy adults to simulate infection in their bodies. The subjects were monitored seven days before inoculation and five days after. Participants wore a smart ring, smart watch, and a smart T-shirt simultaneously throughout the study. As well, researchers collected biomarkers of systemic inflammation using blood samples, PCR tests to detect the presence of respiratory pathogens, and a mobile app to collect symptoms reported by participants. In total, more than two billion data points were collected to train machine learning algorithms. Ten different AI models were developed, but the researchers chose the model that used the least amount of data for the remainder of the project. The chosen model correctly detected nearly 90 per cent of actual positive cases and was deemed more practical for daily monitoring. On their own, Jensen said, none of the data collected from the ring, watch, or T-shirt alone is sensitive enough to detect how the body is responding. 'An increase in heart rate alone may only correspond to two beats per minute, which is 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 … several different measurements, we would be able to identify subtle changes in physiology.' Wednesdays What's next in arts, life and pop culture. The algorithms also successfully detected systemic inflammation in four participants infected with COVID-19 during the study. In each case, the algorithms flagged the immune response up to 72 hours before symptoms appeared or infection was confirmed by PCR testing. Ultimately, the researchers hope to develop a system that will inform patients of possible inflammation so they can contact their health-care provider. 'In medicine, we say that you have to give the right treatment to the right person at the right time,' Jensen said. By expanding the therapeutic window in which doctors can intervene, he added, they could save lives and achieve significant savings by avoiding hospitalizations and enabling home management of chronic conditions or even aging. 'In a way, we hope to revolutionize personalized medicine.' This report by The Canadian Press was first published July 30, 2025.
New Indian Express
a day ago
- Health
- New Indian Express
Surrogacy vs mental health
From Shah Rukh Khan and Gauri Khan (AbRam) to Aamir Khan and Kiran Rao (Azad), Tusshar Kapoor (Laksshya), and Priyanka Chopra and Nick Jonas (Malti Marie), several celebrities have turned to surrogacy to build their families. As public awareness grows and stigma fades, surrogacy has become a more accepted reproductive option, especially in cases where medical complications prevent women from carrying a pregnancy themselves. However, a new study has added an important dimension to this evolving narrative. Published in JAMA Network Open, the landmark research by McGill University and the Institute for Clinical Evaluative Sciences (ICES), Canada, reveals that surrogacy may carry a higher risk of mental health challenges for gestational carriers — commonly known as surrogates. Study at a glance The study analysed an extensive dataset of 7,67,406 pregnancies in Ontario, Canada, between 2012 and 2021. It found that women who carry a pregnancy for others are significantly more likely to develop new-onset mental health conditions during or after pregnancy compared to women who conceive either naturally or via IVF. 'Pregnancy is not just a biological process, it's deeply emotional,' says Dr Sumina Reddy, fertility consultant and director at Fertilica IVF & Women Care, Hyderabad. 'For surrogates, the journey comes with added psychological layers. Even if there is no genetic link to the baby, they still undergo all the hormonal, physical, and emotional changes that pregnancy brings,' she adds. Diagnosed conditions ranged from mood and anxiety disorders to psychosis, substance use, and self-harm. Alarmingly, many diagnoses were made in emergency or inpatient settings, pointing to delayed support and crisis-level interventions. 'Surrogates face a higher likelihood of developing mental health issues compared to women who carry their own children,' notes Dr Sarada Vani N, senior consultant in Obstetrics and Gynaecology, and high-risk pregnancy specialist at Yashoda Hospitals, Hyderabad. 'Mood and anxiety disorders were most common, and the median time from conception to diagnosis was around two and a half years. This shows how some issues may emerge long after the pregnancy ends,' she notes. The doctor adds that surrogates who previously experienced mental health issues were particularly vulnerable. Even when compared to women who conceived without assistance but no longer lived with their child a year after birth, surrogates showed elevated risks, hinting at a grief-like experience akin to that seen in adoption or foster care.
