Title: Machine Learning Strategies for Classification of Peripheral Arterial Disease
- Period: Ongoing
PIs: Wilkins Aquino, Leila Mureebe
- IOMechLab Students: Mark Chen
The goal of this project is the development of a new paradigm for intelligent Continuous Wave Doppler (CWD) ultrasound audio systems capable of accurately staging arterial occlusive disease in lower extremities. To this end, our approach will integrate a wealth of patient data, novel machine learning techniques, uncertainty quantification, computational fluids dynamics, and computational acoustics with existing CDW audio technology to produce a new generation of devices that can intelligently assist clinical care providers in accurately diagnosing arterial occlusive disease.