The 2024 Research Initiative to promote Diversity in Radiology was a success! The UCSF Center for Intelligent Imaging (ci2) had several intern member representatives, including Nick Xing, Nina Phatak, Rawan Khalifa and Henry Salkever.
Presenters were tasked with promoting radiology as a career choice and diversifying the imaging professionals through innovative research.
Nick Xing, who studies at the University of Illinois at Urbana-Champaign, presented on "API-driven Batch Processing for Longitudinal Hepatocellular Carcinoma Surveillance." Using the Retrieval-Augmented Generation (RAG), Xing and his collaborators analyzed the efficacy of large language models (LLMs) tasked with instructions from the LI-RADS v2018 CT/MRI Core document. The original results without using RAG had inaccuracies.
Nina Phatak presented "Stratifying Chronic Lower Back Pain Subtypes Using Machine Learning on Resting-State fMRI Data." Implementing bold fMRI signals, Nina and her collaborators aimed to classify high and low pain types in patients with chronic lower back pain. Pre-trained deep learning models extracted relevant learning features from scalogram images. Nina is an undergraduate student at the University of California, Riverside.
Rawan Khalifa, studying at Minerva University, resented "Quantitative Assessment of Lead Location in Deep Brain Stimulation: Implications for Clinical Outcomes in Parkinson's Disease." Rawan's research aimed to integrate quantitative metrics into predictive models alongside other imaging and clinical data to understand better how lead placement influences the efficacy of deep brain stimulation.
Henry Salkever, a University of North Carolina Wilmington student, presented on the automation of identifying regions of interest in tumor imaging. Henry developed a framework for clinicians to use and implement the model's predictions.
Congratulations to the ci2 interns on a successful summer of academic achievement and innovative research!
