Researchers at McGill University have developed an innovative AI model capable of detecting infections before symptoms manifest, focusing on physiological measures rather than traditional symptom reporting.

This groundbreaking technology targets early immune responses to viral respiratory tract infections (VRTIs) by utilizing data from wearable devices such as smart rings, watches, and shirts.

The study involved monitoring 55 healthy adults who received a live attenuated influenza vaccine, allowing researchers to simulate infection and collect over two billion data points for their AI algorithms.

The findings of this research were published in The Lancet Digital Health journal, marking a significant advancement in predictive health technology.

The project reflects a collaborative effort among experts in clinical physiology, biomedical engineering, infectious diseases, and AI, emphasizing the potential for significant advancements in proactive healthcare.

This innovative approach aims to detect silent signs of infection, specifically acute systemic inflammation associated with VRTIs, enabling earlier intervention and potentially saving lives.

The chosen AI model demonstrated the capability to identify systemic inflammation in COVID-19 infected participants up to 72 hours before symptoms appeared or prior to PCR confirmation.

The research has led to the establishment of Sensifai Health Inc., a startup focused on commercializing this AI-driven health monitoring technology.

Acute systemic inflammation can lead to severe complications, especially in vulnerable populations, making early detection crucial for preventing serious health outcomes.

This innovation could lead to significant improvements in personalized medicine, potentially saving lives and reducing healthcare costs by preventing hospitalizations and allowing better management of chronic conditions.

The AI model analyzes various physiological metrics, including heart rate, heart rate variability, body temperature, respiratory rate, and blood pressure.

By broadening the therapeutic window for medical intervention, the researchers believe their work could save lives, reduce hospitalization costs, and improve management of chronic conditions.