Applications of machine learning in urodynamics: A narrative review

Area of Science:

• Urology
• Medical Informatics
• Computational Science

Background:

• Machine learning (ML), encompassing traditional and deep learning, is increasingly utilized in urodynamics research.
• Current literature lacks guidance on selecting appropriate ML models for diverse urodynamic research tasks.

Purpose of the Study:

• To summarize and classify ML algorithms applied in the field of urodynamics.
• To provide guidance for researchers in selecting optimal ML models for specific urodynamic research requirements.

Main Methods:

• A narrative review of published literature on ML in urodynamics.
• Searches conducted in PubMed up to December 2023, limited to English language.
• Keywords included: artificial intelligence, machine learning, deep learning, urodynamics, lower urinary tract symptoms.

Main Results:

• ML applications in urodynamics cover examination, diagnosis of urinary tract dysfunction, and treatment efficacy prediction.
• Most studies are single-center, retrospective, and lack external validation, indicating a need for improved model generalization.
• Research is in early stages, with few high-quality multi-center studies and a need for model optimization before clinical application.

Conclusions:

• No existing research comprehensively analyzes ML algorithms in urodynamics.
• This review aims to fill this gap by classifying ML applications.
• The findings will assist researchers in choosing suitable ML models for improved urodynamic research outcomes.