Machine Learning for Urodynamic Detection of Detrusor Overactivity.

Journal: Urology
PMID: 34757048

Abstract

OBJECTIVE: To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to significant variability in individual interpretations of UDS data, there is a need to standardize UDS interpretation.

Authors

  • Kevin T Hobbs
    Division of Urologic Surgery, Duke University Medical Center, Durham, NC.
  • Nathaniel Choe
    Department of Electrical and Computer Engineering, Duke University, Durham, NC.
  • Leonid I Aksenov
    Division of Urologic Surgery, Duke University Medical Center, Durham, NC.
  • Lourdes Reyes
    Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC.
  • Wilkins Aquino
    Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC.
  • Jonathan C Routh
    Division of Urologic Surgery, Duke University Medical Center, Durham, NC.
  • James A Hokanson
    Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI. Electronic address: jhokanson@mcw.edu.

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