ArticleSensors (Basel, Switzerland)2021
Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- A Model to Define Reference Ultrasound Parameters for Early Assessment of Nephron Endowment in Extremely Low Birth Weight Preterm Infants.Children (Basel, Switzerland) · 2026Article
- Neonatal Renal Ultrasound Reference Values in Romanian Term Newborns: Correlations with Anthropometric Characteristics.Children (Basel, Switzerland) · 2025Article
- Deep learning in renal ultrasound: applications, challenges, and future outlook.Frontiers in oncology · 2025Review
- Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet+.Scientific reports · 2024Article
- The correlation between GFR and unit renal volume in infants with hydronephrosis measured by two imaging methods.Scientific reports · 2023Article
- What to do with kidney length and volumes in large individuals?Pediatric nephrology (Berlin, Germany) · 2023Article
- A narrative review on the application of artificial intelligence in renal ultrasound.Frontiers in oncology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
In this study, we aimed to develop a new automated method for kidney volume measurement in children using ultrasonography (US) with image pre-processing and hybrid learning and to formulate an equation to calculate the expected kidney volume. The volumes of 282 kidneys (141 subjects, <19 years old) with normal function and structure were measured using US. The volumes of 58 kidneys in 29 subjects who underwent US and computed tomography (CT) were determined by image segmentation and compared to those calculated by the conventional ellipsoidal method and CT using intraclass correlation coefficients (ICCs). An expected kidney volume equation was developed using multivariate regression analysis. Manual image segmentation was automated using hybrid learning to calculate the kidney volume. The ICCs for volume determined by image segmentation and ellipsoidal method were significantly different, while that for volume calculated by hybrid learning was significantly higher than that for ellipsoidal method. Volume determined by image segmentation was significantly correlated with weight, body surface area, and height. Expected kidney volume was calculated as (2.22 × weight (kg) + 0.252 × height (cm) + 5.138). This method will be valuable in establishing an age-matched normal kidney growth chart through the accumulation and analysis of large-scale data.
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Registered trials
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