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Applications of Artificial Intelligence in Urodynamic Data Interpretation: A Narrative Review
Farzad Pourghazi1, Pradeep Kumar Chaudhary1, Brian J Linder2
1Physiology and Biomedical Engineering Department, Mayo Clinic College of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Neurourology and Urodynamics
|May 12, 2026
Summary
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), shows promise for automated urodynamic studies (UDS) interpretation. While technically strong, AI requires standardized data and validation for widespread clinical use.
Area of Science:
- Urology and Medical Informatics
- Application of Artificial Intelligence in Healthcare
- Lower Urinary Tract Dysfunction Diagnostics
Background:
- Urodynamic studies (UDS) are crucial for diagnosing lower urinary tract dysfunction but are complex and prone to variability.
- Artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer potential for automated, objective, and efficient UDS interpretation.
- Multichannel pressure-flow data analysis is a key area for AI application in UDS.
Purpose of the Study:
- To review current applications of ML and DL in multichannel UDS interpretation.
- To evaluate the performance, clinical targets, and limitations of AI in UDS.
- To critically assess the state of AI-driven UDS analysis.
Main Methods:
- Comprehensive literature search of PubMed, Scopus, and Web of Science up to September 2, 2025.
- Narrative review of peer-reviewed studies utilizing ML or DL for invasive UDS signal analysis (pressures and flow).
- Systematic assessment of model design, input features, validation, and clinical outcomes, excluding studies limited to uroflowmetry.
Main Results:
- Twelve studies met inclusion criteria, applying AI to diverse UDS tasks like detrusor overactivity detection and bladder outlet obstruction classification.
- AI models demonstrated high diagnostic performance, with accuracies/AUCs often between 80%-95% for primary outcomes.
- Significant heterogeneity in study design, reliance on retrospective/single-center data, and limited external validation were noted.
Conclusions:
- ML and DL effectively extract meaningful data from UDS, showing strong technical performance for diagnostic tasks.
- Current limitations include methodological variability, restricted generalizability, and a lack of prospective validation, hindering clinical adoption.
- AI-based UDS interpretation is an emerging field necessitating standardized data, robust validation, and clinical workflow integration.
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