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Article

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

by
Dong-Wook Kim
1,†,
Hong-Gi Ahn
1,†,
Jeeyoung Kim
1,
Choon-Sik Yoon
2,
Ji-Hong Kim
3,* and
Sejung Yang
1,*
1
Department of Biomedical Engineering, Yonsei University, Wonju 26494, Korea
2
Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea
3
Department of Pediatrics, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2021, 21(20), 6846; https://doi.org/10.3390/s21206846
Submission received: 3 August 2021 / Revised: 4 October 2021 / Accepted: 12 October 2021 / Published: 14 October 2021
(This article belongs to the Collection Artificial Intelligence (AI) in Biomedical Imaging)
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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.
Keywords: kidney volume measurement; ultrasonography; image segmentation; artificial intelligence; hybrid learning kidney volume measurement; ultrasonography; image segmentation; artificial intelligence; hybrid learning

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MDPI and ACS Style

Kim, D.-W.; Ahn, H.-G.; Kim, J.; Yoon, C.-S.; Kim, J.-H.; Yang, S. Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children. Sensors 2021, 21, 6846. https://doi.org/10.3390/s21206846

AMA Style

Kim D-W, Ahn H-G, Kim J, Yoon C-S, Kim J-H, Yang S. Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children. Sensors. 2021; 21(20):6846. https://doi.org/10.3390/s21206846

Chicago/Turabian Style

Kim, Dong-Wook, Hong-Gi Ahn, Jeeyoung Kim, Choon-Sik Yoon, Ji-Hong Kim, and Sejung Yang. 2021. "Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children" Sensors 21, no. 20: 6846. https://doi.org/10.3390/s21206846

APA Style

Kim, D.-W., Ahn, H.-G., Kim, J., Yoon, C.-S., Kim, J.-H., & Yang, S. (2021). Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children. Sensors, 21(20), 6846. https://doi.org/10.3390/s21206846

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