Classification of Vascular Anomalies using Continuous Doppler Ultrasound and Machine Learning

Overview

Overview: Over ten weeks, BME and ECE majors Serge Assaad and Mark Chen joined forces with Mechanical Engineering Masters student Guangshen Ma to automate the diagnosis of vascular anomalies from Doppler Ultrasound data, with goals of improving diagnostic accuracy and reducing physician time spent on simple diagnoses. They worked closely with Duke Surgeon Dr. Leila Mureebe and Civil and Environmental Engineering Professor Wilkins Aquino.

Project Results: Working with a Duke Hospital dataset of 5 Doppler ultrasound recordings taken from each of 38 patients, the team used machine-learning to predict whether or not a patient was healthy from the recordings. They extracted features from the recordings using a variety of tools from signal processing, visualized the separation of healthy and unhealthy patients in this feature space, and built a competitive classifier using standard supervised-learning tools. They had the opportunity to present their findings to Duke's Provost and to senior leadership within Duke Hospital and the Duke Clinical Research Institute.

Faculty Leads: Wilkins Aquino, Leila Mureebe

Project Manager: Kyle Burris

Click here for the Executive Summary

"Participating in Data+ definitely changed my perception of Data Science research. It was more interdisciplinary than I expected, and the opportunity to work with experts across different fields (Medicine, Civil Engineering, Statistics) was a defining aspect of my Data+ experience." - Serge Assad, Biomedical Engineering, Electrical & Computer Engineering

"The project mentor was fantastic. The three students I worked with were superb. We were able to make great progress that will lead to journal publications and grant proposals." — Wilkins Aquino, Professor in the Department of Civil and Environmental Engineering. Pratt School of Engineering

Contact

Ashlee Valente , Duke Center for Applied Genomics & Precision Medicine
ashlee.valente@duke.edu

Topics

Data+