Classification of Vascular Anomalies using Continuous Doppler Ultrasound and Machine Learning

Project Summary

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.

Themes and Categories
Health
Data+
Year
2017
Contact
Ashlee Valente
Duke Center for Applied Genomics & Precision Medicine
ashlee.valente@duke.edu

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

Related People

Related Projects

Getting Granular on Social Determinants of Health

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: Austin Talbot

Investigating oil and gas production in the United Kingdom

Aaron Chai (Computer Sciece, Math) and Victoria Worsham (Economics, Math) spent ten weeks building tools to understand characteristics of successful oil and gas licenses in the North Sea. The team used data-scraping, merging, and OCR method to create a dataset containing license information and work obligations, and they also produced ArcGIS visualizations of license and well locations. They had the chance to consult frequently with analytics professionals at ExxonMobil.

Click here to read the Executive Summary

 

Project Lead: Kyle Bradbury

Project Manager: Artem Streltsov

On the Shelf: exploring oil and gas production in the Gulf of Mexico

Yueru Li (Math) and Jiacheng Fan (Economics, Finance) spent ten weeks investigating abnormal behavior by companies bidding for oil and gas rights in the Gulf of Mexico. Working with data provided by the Bureau of Ocean Energy Management and ExxonMobil, the team used outlier detection methods to automate the flagging of abnormal behavior, and then used statistical methods to examine various factors that might predict such behavior. They had the chance to consult frequently with analytics professionals at ExxonMobil.

 

Click here to read the Executive Summary

 

Project Lead: Kyle Bradbury

Project Manager: Hyeongyul Roh

See all Research