Development and validation of an automated diagnostic tool for wound imaging

Over 6.5 million people in North America live with chronic wounds which pose a burden on their quality of life and the healthcare system. Chronic wounds are estimated to cost over $30 billion per year. Swift Medical is a pioneer in point-of-care imaging for wounds. Their mobile apps allow the reliable and accurate measurement of wound characteristics, making it an ideal tool to track healing and identify healing patterns. Using artificial intelligence/machine learning and a large database of wound data that Swift Medical uniquely possess, we propose the development of a diagnostic tool to classify wound images and its validation in an independent cohort composed of patients receiving care by Professor Gregory Berry at the McGill University Health Center. The resulting algorithms will be used by Swift to enhance the capabilities for their mobile technology, which could improve patient care by monitoring patients at high-risk of chronic wounds, such as people with diabetes or impaired mobility, promote widespread access to telemedicine in remote communities, and reduce the overall cost of chronic wound treatment to the healthcare system.

Faculty Supervisor:

Gregory Berry

Student:

Partner:

Swift Medical

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

Research Institute of the McGill University Health Centre

Program: