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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children
Author
Dong-Wook, Kim 1 ; Hong-Gi Ahn 1   VIAFID ORCID Logo  ; Kim, Jeeyoung 1 ; Choon-Sik Yoon 2   VIAFID ORCID Logo  ; Ji-Hong, Kim 3   VIAFID ORCID Logo  ; Yang, Sejung 1   VIAFID ORCID Logo 

 Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea; [email protected] (D.-W.K.); [email protected] (H.-G.A.); [email protected] (J.K.) 
 Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea; [email protected] 
 Department of Pediatrics, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea 
First page
6846
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2584580055
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.