Automated diagnosis and prognostication of severity in COPD via deep learning frameworks using multi-modal data

Chronic Obstructive Pulmonary Disease (COPD) is a progressive, debilitating, chronic respiratory disease. It is currently the 4th leading cause of mortality and is responsible for 100,000 hospitalizations and 10,000 deaths annually in Canada, and 3 million deaths worldwide. Although our understanding of COPD pathogenesis has improved substantially over the past 20 years, there is a notable lack of treatments that can modify disease progression and reduce mortality. Furthermore, current methods to clinically diagnose COPD are non-specific and insufficient to advance knowledge. This project will build on the recent successes of advanced machine learning (ML) techniques applied to automated image analyses of medical scans, in various medical fields, to improve COPD diagnosis and prognostication. Specifically, this project will implement and test new frameworks based on deep learning (DL) to automate staging of COPD disease severity and to predict disease progression by using multi-modal and/or heterogeneous data (e.g., non-imaging based and imaging-based data). The outcome of this project is the development of new machine learning tools to better support clinicians treating COPD patients.