Artificial Intelligence for Predicting Treatment Failure in Neurourology: From Automated Urodynamics to Precision Management

Artificial intelligence (AI) has emerged as a transformative tool for advancing diagnosis, monitoring, and treatment planning in neurourology. This review synthesizes recent progress in AI-based models for predicting treatment failure in neurogenic lower urinary tract dysfunction. Machine learning a...

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Published in:International neurourology journal pp. 55 - 64
Main Authors: Seunghyun Youn, Beom Jin Park
Format: Journal Article
Language:English
Published: 대한배뇨장애요실금학회 01.11.2025
Subjects:
비뇨기과학
ISSN:2093-4777, 2093-6931
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Abstract Artificial intelligence (AI) has emerged as a transformative tool for advancing diagnosis, monitoring, and treatment planning in neurourology. This review synthesizes recent progress in AI-based models for predicting treatment failure in neurogenic lower urinary tract dysfunction. Machine learning and deep learning algorithms applied to urodynamic, clinical, and neuroimaging data have demonstrated strong potential to identify patients at risk of therapeutic nonresponse and improve individualized management. Automated systems now enable precise interpretation of complex bladder signals, multimodal data integration, and real-time prediction of treatment outcomes, marking a shift toward data-driven precision medicine. Nevertheless, most published studies remain limited by small, single-center datasets and a lack of external validation. Broader clinical adoption will require multicenter collaboration, adherence to standardized reporting frameworks such as TRIPOD-ML and PRO BAST-AI, and integration of explainable AI to ensure transparency, reproducibility, and clinician trust. KCI Citation Count: 0
AbstractList Artificial intelligence (AI) has emerged as a transformative tool for advancing diagnosis, monitoring, and treatment planning in neurourology. This review synthesizes recent progress in AI-based models for predicting treatment failure in neurogenic lower urinary tract dysfunction. Machine learning and deep learning algorithms applied to urodynamic, clinical, and neuroimaging data have demonstrated strong potential to identify patients at risk of therapeutic nonresponse and improve individualized management. Automated systems now enable precise interpretation of complex bladder signals, multimodal data integration, and real-time prediction of treatment outcomes, marking a shift toward data-driven precision medicine. Nevertheless, most published studies remain limited by small, single-center datasets and a lack of external validation. Broader clinical adoption will require multicenter collaboration, adherence to standardized reporting frameworks such as TRIPOD-ML and PRO BAST-AI, and integration of explainable AI to ensure transparency, reproducibility, and clinician trust. KCI Citation Count: 0
Author Beom Jin Park
Seunghyun Youn
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  organization: (Department of Radiology and Advanced Medical Imaging Institute, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea)
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Title Artificial Intelligence for Predicting Treatment Failure in Neurourology: From Automated Urodynamics to Precision Management
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