Pourghazi, Farzad

ABSTRACT Background Interpretation of urodynamic studies (UDS) is central to diagnosing lower urinary tract dysfunction, but it is often complex, time‐consuming, and subject to interobserver variability. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled automated analysis of multichannel pressure–flow data and may improve objectivity and efficiency in UDS interpretation. Objective To critically review current applications of ML and DL for interpretation of multichannel UDS data and to evaluate their performance, clinical targets, and limitations. Methods A literature search of PubMed, Scopus, and Web of Science was performed through September 2, 2025, without language or date restrictions. A narrative review was conducted of peer‐reviewed studies applying ML or DL techniques to invasive UDS signals, including vesical, abdominal, and detrusor pressures as well as urinary flow. Studies limited to uroflowmetry or non‐UDS inputs were excluded. Model design, input features, validation strategies, and clinical outcomes were systematically assessed. Results A total of 12 studies met inclusion criteria. AI models were applied to a range of UDS tasks, including detection of detrusor overactivity, classification of bladder outlet obstruction and detrusor underactivity, multi‐feature UDS pattern recognition, severity grading, and real‐time event detection. Reported diagnostic performance was generally high, with many models achieving accuracies or AUCs between 80% and 95% for primary outcomes. However, study designs were heterogeneous, most datasets were retrospective and single‐center, and external validation was limited. Conclusions ML and DL approaches can extract clinically meaningful information from multichannel UDS recordings and demonstrate strong technical performance across several diagnostic tasks. However, variability in methodology, limited generalizability, and lack of prospective validation currently limit clinical adoption. AI‐based UDS interpretation remains an emerging research area requiring standardized data, robust validation, and integration with clinical workflows.

ABSTRACT Background Urodynamic studies (UDS) are integral to the evaluation and management of lower urinary tract dysfunction. However, artifacts are frequently encountered, affecting both clinical interpretation and research reliability. Objective This expert narrative review examines the prevalence, classification, and clinical relevance of artifacts encountered during UDS, based on findings from the existing literature. Methods A focused literature review was conducted, analyzing studies that reported data on the sources and impact of UDS artifacts, particularly from the patient perspective. Emphasis was placed on identifying both technical and patient‐related contributors to artifact generation. Results Artifacts are common in UDS despite adherence to Good Urodynamic Practices recommended by the International Continence Society. Technical artifacts may stem from equipment‐related issues such as calibration errors, catheter displacement, and device variability. Patient‐related artifacts can arise from physical movement or test‐induced detrusor overactivity. These artifacts may lead to misinterpretation, misdiagnosis, and inappropriate interventions in clinical practice. Furthermore, they may affect the validity of research findings related to diagnostic tools for lower urinary tract symptoms. Conclusion Identifying and addressing UDS artifacts during the procedure or in post‐test interpretation is critical for accurate clinical decision‐making and reliable research outcomes. Efforts to minimize artifacts should be prioritized to improve the quality and utility of UDS.