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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. 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...

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Detalles Bibliográficos
Autores principales: Sloan, Matthew, Li, Hui, Lescay, Hernan A., Judge, Clark, Lan, Li, Hajiyev, Parviz, Giger, Maryellen L., Gundeti, Mohan S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Urological Association 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630684/
https://www.ncbi.nlm.nih.gov/pubmed/37932570
http://dx.doi.org/10.4111/icu.20230170
Descripción
Sumario: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.