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

Area of Science:

• Neurourology
• Artificial Intelligence
• Medical Informatics

Background:

• Artificial intelligence (AI) is revolutionizing healthcare, offering advanced tools for diagnosis, monitoring, and treatment planning in neurourology.
• Predicting treatment failure in neurogenic lower urinary tract dysfunction (NLUTD) is crucial for effective patient management.

Purpose of the Study:

• To review recent advancements in AI models for predicting treatment failure in NLUTD.
• To highlight the potential of machine learning and deep learning in identifying patients at risk of therapeutic nonresponse.

Main Methods:

• Application of machine learning and deep learning algorithms.
• Utilizing urodynamic, clinical, and neuroimaging data.
• Development of automated systems for signal interpretation and data integration.

Main Results:

• AI models demonstrate strong potential in identifying patients likely to experience treatment failure.
• Automated systems facilitate precise interpretation of bladder signals and multimodal data integration.
• AI enables real-time prediction of treatment outcomes, advancing precision medicine.

Conclusions:

• AI offers significant potential for improving individualized management of NLUTD.
• Challenges include small datasets, lack of external validation, and the need for explainable AI.
• Multicenter collaboration and standardized reporting are essential for clinical adoption.