Investig Clin Urol.  2023 Nov;64(6):588-596. 10.4111/icu.20230170.

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

• Sloan M
• Li H
• Lescay HA
• Judge C
• Lan L
• Hajiyev P
• Giger ML
• Gundeti MS

• KMID: 2547433
• DOI: http://doi.org/10.4111/icu.20230170

Abstract

Purpose
Hydronephrosis is a common pediatric urological condition, characterized by dilation of the renal collecting system. Accurate 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 hydronephrosis 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 intervention 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 hydronephrosis. 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.

Keyword

Hydronephrosis; Machine learning; Urology