Projects

Automated medical diagnostic image quality control using AI-based techniques

Members: Dr. Mohamed Shehata, Dr. Rebecca Feldman, Seger Nelson, Anubhav Gupta

The overall goal of the proposed research is to develop a novel solution for performing automated Quality Control (QC), utilizing AI-based analysis of clinical images. Through a partnership with the University of British Columbia (UBC), Advanced Quality Systems (AQS), and Interior Health Authority (IHA), the proposed solution will use artificial intelligence (AI)-based analysis of clinical images that will directly increase the ability and availability of Canadian CT and MRI scanners to be used in patient diagnosis, treatment, research and development. As Canada recovers from the Covid-19 pandemic, this research will help address the increased need for rapid, accurate clinical diagnosis during a period of reduced healthcare staffing levels imposed by infection control measures and staff burnout.

Development and Validation of Image-based Breast Cancer Risk Prediction Model

Members: Dr. Mohamed Shehata, Dr. Rasika Rajapakshe, Tim Mammadov

The primary objective of this project is to evaluate the performance of the image-based Artificial Intelligence (AI) algorithm for breast cancer risk prediction. While cancer risk models have been developed since 1989, they have generally relied on classical statistical models and leveraged risk factors like genetics, family history, hormonal information and mammographic breast density. While they are well calibrated at the population level, they are not accurate at the individual level. Image-based AI models may identify subtle changes in a mammogram that indicate possible development of a sub-clinical cancer, and thereby that lead to its enhanced performance over other traditional risk models. For example, screening mammography has a significant false-positive rate. Such results can be very stressful to patients, and have led to a reduction in a woman’s likelihood of attending subsequent screens. However, false positive mammograms have been found to significantly increase the relative risk of detecting cancer at subsequent screening in three organized screening programs for three different countries (Denmark, Spain, and the United Kingdom). Most importantly, these relative risks are comparable to those attributed to family history, one of the strongest risk factors for breast cancer. Here in British Columbia, researchers have found that an abnormal mammogram led to an increased risk of future cancer diagnosis by a factor of 1.73. The increased breast cancer risk in women with false-positive tests may be attributable to misclassification of malignancies already present at the time of the screening as observed on a mammogram by an expert screening radiologist. Of particular interest in the Canadian context is breast screening of First Nations women. Recent studies have found that while First Nations women, on average, have the lowest breast density compared to other ethnic groups, they have an increased incidence of breast cancer. This presents an anomaly as previous studies have found that women with higher breast density may be at an increased risk of developing breast cancer compared with those with lowest breast density. Results from this project have the potential to personalize the frequency of a woman’s screening based on her individual risk profile and potentially reduce downstream false positive recalls. We believe the use of this type of image-based risk model will eliminate any potential bias against First Nations and other ethnic women, and improve accuracy in predicting breast cancer risk. The results of this study have the potential to inform practice changes for breast screening programs across Canada.