Peripheral edema is the most common symptom of heart failure. Phenotyping of and continuous monitoring of edema provides critical clinical information and can be used for averting episodes of acute decompensation and hospitalizations. Students will do research on applying video-based AI techniques to measure, estimate and track edema grades. They will use videos of skin during the edema pitting-test, acquired from models and from real patients. They will design and experiment with deep learning models and video preprocessing techniques. The focus of the work during the spring of 2020 will be data augmentation based on videos acquired from patients.
Name of Lab: Kostic lab
Dates: 6/1/2020 - 8/21/2020
Direct Supervisor: Zoran Kostic
Hours per week: 20
Number of positions: 1
Skills required: signal processing, video processing, deep learning frameworks, software engineering, real-time embedded coding.
Eligibility: Junior, Senior, Master's