Machine Learning for Urodynamic Detection of Detrusor Overactivity.

Journal Article (Journal Article)

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. MATERIALS AND METHODS: Patients who underwent UDS at a single pediatric urology clinic between May 2012 and September 2020 were included. UDS files were analyzed in both time and frequency domains, varying inclusion of vesical, abdominal, and detrusor pressure channels. A machine learning pipeline was constructed using data windowing, dimensionality reduction, and support vector machines. Models were designed to detect clinician identified detrusor overactivity. RESULTS: Data were extracted from 805 UDS testing files from 546 unique patients. The generated models achieved good performance metrics in detecting DO agreement with the clinician, in both time- and frequency-based approaches. Incorporation of multiple channels and data windowing improved performance. The time-based model with all 3 channels had the highest area under the curve (AUC) (91.9 ± 1.3%; sensitivity: 84.2 ± 3.8%; specificity: 86.4 ± 1.3%). The 3-channel frequency-based model had the highest specificity (AUC: 90.5 ± 1.9%; sensitivity: 68.3 ± 5.3%; specificity: 92.9 ± 1.1%). CONCLUSION: We developed a promising proof-of-concept machine learning pipeline that identifies DO in UDS. Machine-learning-based predictive modeling algorithms may be employed to standardize UDS interpretation and could potentially augment shared decision-making and improve patient care.

Full Text

Duke Authors

Cited Authors

  • Hobbs, KT; Choe, N; Aksenov, LI; Reyes, L; Aquino, W; Routh, JC; Hokanson, JA

Published Date

  • January 2022

Published In

Volume / Issue

  • 159 /

Start / End Page

  • 247 - 254

PubMed ID

  • 34757048

Pubmed Central ID

  • PMC8865755

Electronic International Standard Serial Number (EISSN)

  • 1527-9995

Digital Object Identifier (DOI)

  • 10.1016/j.urology.2021.09.027

Language

  • eng

Conference Location

  • United States