Sunnybrook researchers developing new artificial intelligence platform for precision medicine

A $485,676 grant from the Canada Foundation for Innovation (CFI) will help to establish the Artificial Intelligence Platform for Precision Medicine (AIPPM) at Sunnybrook Research Institute.

The project, which includes a new state-of-the-art computing system designed to handle massive amounts of imaging data, will enable researchers to carry out transformative, translational work in the field of deep learning applied to medical imaging and image-guided therapy.

“At Sunnybrook we collect an enormous amount of data in the form of medical images, radiologists and pathologists reports and treatment outcomes,“ says Dr. Anne Martel, a senior scientist in physical sciences at Sunnybrook Research Institute and principal investigator of the project. “AI can help us to discover new connections and insights within this data that will allow us to predict patient outcomes, guide therapy decisions and improve the delivery of therapy.”

The research team, led by Dr. Martel, will use anonymized patient data — in the form of X-rays, CT scans, MR images and digital pathology — to develop and train artificial intelligence (AI) models that will eventually be used to solve pressing clinical problems across multiple clinical areas at Sunnybrook. The platform will be a significant step forward for clinical-grade AI systems in Canada.

“AI systems for medical data have typically been slow to progress for a few reasons — medical images are much larger than those traditionally used to train deep learning algorithms, very large datasets are needed to capture the variability in patient populations, and there is a need to link the imaging to clinical data whilst still protecting personal health information,” says Dr. Martel, who is also a professor of Medical Biophysics at the University of Toronto.

The team at Sunnybrook has already developed innovative AI approaches for a range of medical applications. The computational power provided by the new computer systems will enable the team to accelerate experiments and move past existing memory constraints to explore new deep learning architectures. This capability will be particularly useful for applications involving large data volumes, such as digital pathology applications where a single image is made up of thousands of individual tiles.

The team of collaborators working on the AIPPM project includes scientists and clinicians from a wide range of programs at Sunnybrook. Their expertise in translational research will ensure that these advances are applied to a number of clinical challenges across disorders and systems, and will be used to improve patient care. The team at Sunnybrook is also collaborating with computer scientists at the Vector Institute to translate state-of-the-art deep learning methods from the computer vision community into the medical imaging domain.