Abstract
In this presentation, I will cover how I have used AI and machine learning solutions in several healthcare-related contexts. I will start by describing lung cancer research I performed along with radiation oncologists at Orlando Health. We studied the response of tumors to Stereotactic Body Radiotherapy (SBRT) and developed a novel metric for three-year overall patient survival. Next, in the context of pathology, I will present results from a collaboration on rhabdomyosarcoma (RMS) with scientists at the National Cancer Institute and the Frederick National Laboratory for Cancer Research. RMS is a rare pediatric cancer that poses a challenge for AI tools because there is little source data to learn from. I will discuss how we curated and released the first widely-available pathology dataset for RMS along with trained AI models for tissue segmentation, genetic mutation, and event-free survival. Finally, I will present early work studying the organization of cellular neighborhoods during SIV (simian immunodeficiency virus) infection using large, multi-channel microscopy images. This project is also being conducted along with the Frederick National Lab. Along the way, we will discuss emerging trends in AI as they related to applied research in healthcare.
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