Artificial intelligence (AI) has emerged as a promising tool to enhance the diagnostic precision, interpretation, and treatment prediction in urodynamic studies. This systematic review aims to evaluate the current role of AI in neurourology, specifically focusing on its applications in diagnosing lower urinary tract dysfunction (LUTD), automating urodynamic interpretation, and forecasting treatment outcomes.

A systematic review was conducted in accordance with PRISMA guidelines. Databases searched included PubMed, Scopus, and Google Scholar for peer-reviewed articles published between January 2015 and January 2025. Search terms encompassed combinations of: “artificial intelligence,” “machine learning,” “deep learning,” “urodynamics,” “urodynamic studies,” “diagnosis,” “interpretation,” and “prediction.” Inclusion criteria were original studies applying AI to urodynamic data in patients with LUTD, including neurogenic bladder, detrusor overactivity (DO), detrusor underactivity (DU), bladder outlet obstruction (BOO), or related conditions. Exclusion criteria included animal studies, theoretical models without clinical validation, and articles not in English. Data extraction included AI methodology, urodynamic parameters, outcome metrics, and clinical relevance. No new clinical data were collected.

Twenty studies encompassing 15,958 patients were included, with most applying machine learning or deep learning algorithms to pressure-flow data, uroflowmetry, or combined parameters. Diagnostic applications focused on DU (AUC range: 0.72–0.93), BOO (AUC range: 0.72–0.95), and DO (AUC range: 0.84–0.92). AI models consistently outperformed or matched clinician performance in sensitivity and specificity. CNNs, SVMs, random forests, and ensemble models were most commonly employed. Studies on automated uroflowmetry interpretation showed classification accuracies up to 96.7%. Predictive models demonstrated high accuracy in anticipating responses to botulinum toxin and neuromodulation (AUCs up to 0.96). Overall, AI achieved a mean diagnostic accuracy of 88.1%, mean sensitivity of 88.2%, and specificity of 83.0%. However, most studies were retrospective, single-centre, and lacked external validation.

AI demonstrates robust potential in enhancing urodynamic analysis by reducing interobserver variability, automating interpretation, and enabling non-invasive diagnostics. AI’s ability to integrate complex datasets facilitates more standardized assessments across diverse patient populations. Despite high performance metrics, challenges remain, including variability in diagnostic thresholds, lack of model interpretability, and limited multicentre validation. Adoption in clinical practice will require transparent algorithm development, interdisciplinary collaboration, and prospective trials to confirm clinical utility and generalizability.

AI offers a transformative pathway toward accurate, efficient, and individualized diagnosis and management of lower urinary tract dysfunction. While current models show high performance across multiple diagnostic categories, integration into routine urodynamic practice will depend on standardized data inputs, prospective validation, and clinical interpretability. With continued development, AI has the potential to make urodynamics more accessible, reproducible, and predictive in functional and neurourology.

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