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Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children

 Dong-Wook Kim  ;  Hong-Gi Ahn  ;  Jeeyoung Kim  ;  Choon-Sik Yoon  ;  Ji-Hong Kim  ;  Sejung Yang 
 SENSORS, Vol.21(20) : 6846, 2021-10 
Journal Title
Issue Date
Adult ; Artificial Intelligence* ; Child ; Humans ; Image Processing, Computer-Assisted ; Kidney / diagnostic imaging ; Tomography, X-Ray Computed* ; Ultrasonography ; Young Adult
artificial intelligence ; hybrid learning ; image segmentation ; kidney volume measurement ; ultrasonography
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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1. College of Medicine (의과대학) > Dept. of Pediatrics (소아과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Ji Hong(김지홍) ORCID logo https://orcid.org/0000-0001-5352-5423
Yoon, Choon Sik(윤춘식) ORCID logo https://orcid.org/0000-0003-2010-6710
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