(Tuesday, 7/31/2018) 1:45 PM - 3:45 PM
Room: Davidson Ballroom B
Artificial intelligence applications in radiology and radiation oncology are very rapidly expanding. In addition, there is a rapid increase in research in and applications of radiomics and imaging genomics. Such applications should position radiology and radiation oncology to be enablers of the practice of precision medicine. These applications, in turn, are enabled by robust quantitative imaging biomarker data. In fact, the availability of high quality, vetted quantitative imaging data is key to the training and validation of artificial intelligence applications. This session of the Quantitative Imaging Specialty Track will address applications of AI and imaging genomics in radiology and radiation oncology and how quantitative imaging biomarkers are critically important in such applications.
Learning Objectives:
1. Understand current applications of artificial intelligence methods in radiology and radiation oncology and how quantitative imaging biomarkers are key to such applications.
2. Understand the need for vetted quantitative imaging biomarker and other data that can be used in the training and validation of artificial intelligence applications.
3. Understand selected current and future applications of imaging genomics in radiology and oncology.
Funding Support, Disclosures, and Conflict of Interest: Mayo: Grant support from Varian Medical Systems. Erickson: Grant support from NIDDK (90728). Deasy: Grant support from NIH, the Breast Cancer Research Foundation, Varian, and Philips. Deasy: Disclosure - Co-Founder of PAIGE.AI. Gillies: Investor in and research support from HealthMyne, Inc. and Helix Biopharma.
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