Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential renal function (DRF) <40%, we conducted an analysis of the key parameters of urinary tract ultrasound in children with unilateral hydronephrosis. For children with unilateral hydronephrosis, DRF <40% serves as a compelling indication for surgical intervention, and it can be assessed through diuretic renogram. However, a significant number of patients do not have convenient access to high-quality renograms. So we conducted this analysis aiming to identifying value of urinary tract ultrasound in surgical intervention decision.

METHODS: We retrospectively reviewed the dynamic renogram and urinary tract ultrasound data of 802 children with hydronephrosis presented to our department. Independent risk factors related to DRF <40% were screened out. Several machine learning models were employed. The area under receiving operating curves (AUROC) was calculated for each model to compare their efficiency.

RESULTS: The renal pelvis anterior-posterior diameter, upper calyx dilation, and renal length ratio were found to be independent risk factors for DRF <40%. Among these factors, the renal length ratio had the highest AUROC of 0.820. These 3 factors, alone with the patients' age, were then introduced into 3 machine learning models: random forest, logistic regression, and support vector machines (SVM), among which, the SVM exhibited the highest AUROC of 0.941, with a sensitivity of 90.32% and a specificity of 81.03%.

CONCLUSION: The length ratio exhibited the strongest correlation with DRF <40% among multiple ultrasound indices. Moreover, the SVM model exhibited significant improvement compared to individual factors. Therefore, it can be employed to identify high-risk children with impaired renal function in the assessment of congenital hydronephrosis.