Machine Learning and Artificial Intelligence to Improve Interpretation of Urodynamics

Purpose of Review We sought to review and discuss the current state and future trajectory of machine learning in interpretation of urodynamics studies. We sought to identify the most promising opportunities for improvement in urodynamic interpretation and outcome prediction based on urodynamics using machine learning. Recent Findings Several reports of machine learning algorithms demonstrate accuracy in identification of detrusor overactivity, detrusor underactivity, and other urodynamics phenomena based on tracings. Another series of reports demonstrates that machine learning algorithms incorporating urodynamics factors may accurately predict disease severity or outcomes in functional urologic conditions including overactive bladder and neurogenic lower urinary tract dysfunction. Summary Machine learning has the potential to identify clinically relevant elements such as detrusor overactivity from urodynamics tracings. If externally validated, such an approach could improve efficiency of interpretation and interrater reliability. An important, but more difficult, challenge that would require larger datasets and multi-institution efforts is the application of machine learning to identify clinically relevant urodynamic patterns, unappreciable by humans, that may assist in functional urologic diagnostics, prognostics, and treatment decision-making. In the future, machine learning may realize its potential through integrating clinical factors, test data (including urodynamics with ongoing patient feedback), imaging, biomarkers, and patient preferences, to optimize diagnosis and tailor clinical treatment on a patient-by-patient basis. Clinical Trial Registration This study is not a clinical trial and thus does not warrant registration as such.