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KMID : 1011820230640060588
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Investigative and Clinical Urology
2023 Volume.64 No. 6 p.588 ~ p.596
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Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound
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Matthew Sloan
Hui Li
Hernan A. Lescay
Clark Judge
Li Lan
Parviz Hajiyev
Maryellen L. Giger
Mohan S. Gundeti
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Abstract
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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.
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KEYWORD
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Hydronephrosis, Machine learning, Urology
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