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

Autor: Matthew Sloan, Hui Li, Hernan A. Lescay, Clark Judge, Li Lan, Parviz Hajiyev, Maryellen L. Giger, Mohan S. Gundeti
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Investigative and Clinical Urology, Vol 64, Iss 6, Pp 588-596 (2023)
Druh dokumentu: article
ISSN: 2466-0493
2466-054X
DOI: 10.4111/icu.20230170
Popis: 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
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