Urodynamic Data and Machine Learning

Urodynamic Data and Machine Learning


Nathaniel Choe (ECE) and Mashal Ali (Neuroscience) spent ten weeks developing machine-learning tools to analyze urodynamic detrusor pressure data of pediatric spina bifida patients from the Duke University Hospital. The team built a pipeline that went from raw time series data to signal analysis to dimension reduction to classification, and has the potential to assist in clinician diagnosis.

Click here to read the Executive Summary (PDF)

Faculty Leads: Wilkins AquinoJonathan Routh
Project Manager: Zekun Cao



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