Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound

Purpose: Hydronephrosis is a common pediatric urological condition, characterized by dilation of the renal collecting system. Ac-curate identification of the severity of hydronephrosis is crucial in clinical management, as high-grade hydronephrosis can cause significant damage to the kidney. In this pilot study, we demonstrate the feasibility of machine learning in differentiating between high and low-grade hydronephrosis in pediatric patients. Materials and Methods: We retrospectively reviewed 592 images from 90 unique patients ages 0-8 years diagnosed with hydro-nephrosis at the University of Chicago's Pediatric Urology Clinic. The study included 74 high-grade hydronephrosis (145 images) and 227 low-grade hydronephrosis (447 images). Patients were excluded if they had less than 2 studies prior to surgical interven-tion or had structural abnormalities. We developed a radiomic-based artificial intelligence algorithm incorporating computerized texture analysis and machine learning (support-vector machine) to yield a predictor of hydronephrosis grade. Results: Receiver operating characteristic analysis of the classifier output yielded an area under the curve value of 0.86 (95% CI 0.81-0.92) in the task of distinguishing between low and high-grade hydronephrosis using a five-fold cross-validation by kidney. In addition, a Mann-Kendall trend test between computer output and clinical hydronephrosis grade yielded a statistically significant upward trend (p<0.001). Conclusions: Our findings demonstrate the potential of machine learning in the differentiation between low and high-grade hy-dronephrosis. Further studies are warranted to validate our findings and their generalizability for use in clinical practice as a means to predict clinical outcomes and the resolution of hydronephrosis.