Automated medical diagnostic image quality control using AI-based techniques

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Computed Tomography (CT) and Magnetic resonance imaging (MRI) images are critical diagnostic tools routinely use for diagnosis, treatment planning, and monitoring disease progression. CT and MRI images are particularly critical for evaluating the damage caused to the lungs, brain, and blood vessels, particularly as we are continuing to discover the systemic effects that Covid -19 infections have on the body. The accuracy and timeliness of patient diagnosis on radiology scanners is a direct function of the quality of the images produced during a patient scan. Poor quality images can result in a missed or incorrect diagnosis. In the best-case scenario, an image acquired by a mis-calibrated scanner will result in a costly, time consuming, repeated patient scan, and if undetected it can result in a missed diagnosis which may seriously risk patients' health and, in some cases, lead to death. 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 the 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.