Neural networks outperform expert humans in predicting patient impressions of symptomatic improvement following overactive bladder treatment.

INTRODUCTION AND HYPOTHESIS: The objective was to accurately predict patient-centered subjective outcomes following the overactive bladder (OAB) treatments OnabotulinumtoxinA (OBTX-A) injection and sacral neuromodulation (SNM) using a neural network-based machine-learning approach. In the context of treatments designed to improve quality of life, a patient's perception of improvement should be the gold standard outcome measure. METHODS: Cutting-edge neural network-based algorithms using reproducing kernel techniques were trained to predict patient-reported improvements in urinary leakage and bladder function as assessed by Patient Global Impression of Improvement score using the ROSETTA trial datasets. Blinded expert urologists provided with the same variables also predicted outcomes. Receiver operating characteristic curves and areas under the curve were generated for algorithm and human expert predictions in an out-of-sample holdout dataset. RESULTS: Algorithms demonstrated excellent accuracy in predicting patient subjective improvement in urinary leakage (OBTX-A: AUC 0.75; SNM: 0.80). Similarly, algorithms accurately predicted patient subjective improvement in bladder function (OBTX-A: AUC 0.86; SNM: 0.96). The top-performing algorithms outcompeted human experts across outcome measures. CONCLUSIONS: Novel neural network-based machine-learning algorithms accurately predicted OBTX-A and SNM patient subjective outcomes, and generally outcompeted expert humans. Subtle aspects of the physician-patient interaction remain uncomputable, and thus the machine-learning approach may serve as an aid, rather than as an alternative, to human interaction and clinical judgment.