Kim, Dong-Wook; Ahn, Hong-Gi; Kim, Jeeyoung; Yoon, Choon-Sik; Kim, Ji-Hong; Yang, Sejung
Sensors (Basel, Switzerland)
2021Oct ; 21 ( 20 ) :.
PMID : 34696057
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Kim, Dong-Wook - Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea.
Ahn, Hong-Gi - Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea.
Kim, Jeeyoung - Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea.
Yoon, Choon-Sik - Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea.
Kim, Ji-Hong - Department of Pediatrics, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea.
Yang, Sejung - Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea.
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.
keyword
artificial intelligence; hybrid learning; image segmentation; kidney volume measurement; ultrasonography
MESH
Adult, *Artificial Intelligence, Child, Humans, Image Processing, Computer-Assisted, Kidney/diagnostic imaging, *Tomography, X-Ray Computed, Ultrasonography, Young Adult
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