Validating machine learning models in an independent external cohort: an ensemble model predicted incident hydronephrosis in spina bifida patients with a concordance index of 0.77 and classified bladder dysfunction with 71% accuracy
This study adapted three previously developed machine learning models (a deep learning convolutional neural network using pressure-volume data, a deep learning imaging model using fluoroscopic imaging data, and an ensemble model averaging the risk data from both) to an independent external cohort of spina bifida patients who underwent videourodynamics at a single institution between 2016 and 2025, for prediction of incident hydronephrosis and classification of bladder dysfunction severity; among 70 patients in the hydronephrosis cohort, 13 (18%) developed incident hydronephrosis, the ensemble model achieved a concordance index of 0.77 (pressure-volume only: 0.70, imaging only: 0.72) and an overall AUROC of 0.75 (pressure-volume: 0.67, imaging: 0.