Advisory: Give yourself extra time when travelling by car to Toronto General Hospital, Princess Margaret Cancer Centre, or Toronto Rehab University Centre. City of Toronto construction on University Ave. may cause delays.
At UHN, we strive to deliver Compassionate Care & Caring. Learn more about the services and supports that are available to you throughout your journey.
Our UHN programs and services are among the most advanced in the world. We have grouped our physicians,
staff, services and resources into 10 medical programs to meet the needs of our patients and help us make
the most of our resources.
At the heart of everything we do at UHN are our Healthcare Professionals. Refer a patient to one of our 12 medical programs. Learn more about the resources and opportunities available for professional growth.
University Health Network has grown to be one of the largest research and teaching hospital networks in Canada - pioneers in improving the lives of patients. Our long history of health professions education at Toronto General, Toronto Western, Princess Margaret and Toronto Rehab hospitals has consistently advanced the science of education.
University Health Network is a health care and medical research organization in
Toronto, Ontario, Canada. The scope of research and complexity of cases at UHN has made us a national and international
source for discovery, education and patient care.
Being touched by illness affects us in different ways. Many people want to give back to the community
and help others. At UHN, we welcome your contribution and offer different ways you can help so you can find one that suits you.
The Newsroom is the source for media looking for information about UHN or trying to connect with one
of our experts for an interview. It's also the place to find UHN media policies and catch up on our news stories, videos, media releases,
podcasts and more.
Many emerging technologies such as facial recognition, augmented reality and self-driving cars rely on computer vision. These technologies use computers that can capture, process and interpret images — just like the human eye and brain.
At UHN's KITE Research Institute, Scientist
Dr. Babak Taati and his research team have harnessed computer vision in a pioneering method for differentiating between different types of sleep apneas.
Sleep apnea is a chronic disorder in which breathing intermittently pauses during sleep. This interrupted breathing can dramatically increase the risk of heart disease, stroke and other complications.
Two types are used to categorize sleep apneas: obstructive, in which the throat temporarily collapses, blocking the airway; and central, in which the brain fails to send signals to the muscles that control breathing.
"Distinguishing sleep apneas as either obstructive or central is challenging but crucial to selecting an appropriate treatment," says Dr. Taati. "This is because the treatments vary considerably depending on the type of sleep apnea—for example, continuous positive airway pressure therapy greatly benefits patients with obstructive sleep apneas, but is harmful for those with central sleep apneas."
The new method developed by Dr. Taati's team uses computer vision to monitor a sleeping patient and discern the type of apnea.
Videos of the patient sleeping are recorded with a night vision camera and then analyzed by a computer using artificial intelligence (AI) techniques. By tracking chest and abdomen movements of patients, the computer was able to learn the movements that corresponded to each apnea type.
To validate the method, the research team tested it on patients at KITE's
SleepdB Lab, which is headed by Scientist
Dr. Azadeh Yadollahi. The team found that the computer could differentiate apneas with up to 95% accuracy.
As the first vision-based strategy for distinguishing sleep apneas, the new method does not require any monitoring equipment to be attached to the patient. Unlike other approaches, it does not disrupt the patient's sleeping conditions.
This work was supported by FedDev Ontario, BresoTec Inc, the Natural Sciences and Engineering Research Council of Canada, the Toronto Rehabilitation Institute and Toronto Rehab Foundation.