Session C: 1:45PM – 3:15PM
Health and Medicine. Session C – Oral Presentations, Conference Room, Sill Center
SESSION C (1:45PM – 3:15PM)
Location: Conference Room, Sill Center
Don’t Fear the AI: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology
Alexander Gibson, Brigham Young University
Faculty Mentor Dennis Della Corte, Brigham Young University
SESSION C 1:45-2:00PM
Sill Center
Health and Medicine
Prostate cancer is the second-most diagnosed cancer in men worldwide. It is also the fifth leading cause of death in men. When a patient is referred to a pathologist due to the presence or suspicion of prostate cancer, a pathologist may take a needle core biopsy, which produces six to twelve slides of prostate tissue. The pathologist then stains the tissue (usually using a hematoxylin and eosin stain) and analyzes the tissue samples using a Whole Slide Scanner. The scanner digitizes physical slides into high resolution giga-pixel images. As the shortage of pathologists continues, remaining pathologists are left to analyze more samples than before. Pathologists also face significant inter-observer variability. Thus, the use of artificial intelligence (AI) can drastically increase the productivity and impact of a single pathologist. Researchers across the globe are inventing new machine learning algorithms to detect and classify prostate tissue. This literature review explains how artificial intelligence is being used to detect prostate cancer. After reading over 400 papers, we narrowed our pool of papers down to 142 relevant papers. We classified all machine learning results and methods into 11 different categories (see figure in uploaded PDF). Papers reported algorithm performance according to many different metrics, but we determined that Kappa values, Area under the Curve values, and Accuracy values are the most meaningful metrics. Some papers used a binary classification method to classify tissue, and some papers used a multi-class method. All relevant algorithms were categorized according to their methods and performance. We conclude that pathologists will not be replaced by machine learning algorithms; however, AI can enhance the performance and efficiency of pathologists. An increase in pathologists’ effectiveness could improve access to healthcare in underdeveloped areas and emergent nations.
Chimeric Autoantigen Receptor (CAAR) T cells as a Novel Immunotherapy for Autoreactive B Cells in Graves’ Disease
Mackenzie Hansen, Brigham Young University
Faculty Mentor Kim O’Neill, Brigham Young University
SESSION C 2:05-2:20PM
Sill Center
Health and Medicine
Graves’ Disease is the fourth most common autoimmune disease in the US. The main cause of Graves’ Disease is the overstimulation of the thyroid gland by thyroid stimulating hormone receptor (TSHR) specific antibodies produced by autoreactive B cells. Current therapies for Graves’ Disease include antithyroid drugs, radioiodine therapy, and surgery, but these do not a