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A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs

Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monit...

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Autores principales: Ji, Yewon, Hwang, Gyeongyeon, Lee, Sang Jun, Lee, Kichang, Yoon, Hakyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548669/
https://www.ncbi.nlm.nih.gov/pubmed/37799401
http://dx.doi.org/10.3389/fvets.2023.1236579
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author Ji, Yewon
Hwang, Gyeongyeon
Lee, Sang Jun
Lee, Kichang
Yoon, Hakyoung
author_facet Ji, Yewon
Hwang, Gyeongyeon
Lee, Sang Jun
Lee, Kichang
Yoon, Hakyoung
author_sort Ji, Yewon
collection PubMed
description Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis. However, detecting kidney calculi and monitoring changes in the sizes of the calculi from computed tomography (CT) images is time-consuming for clinicians. This study, in a first of its kind, aims to develop a deep learning model for automatic kidney calculi detection using pre-contrast CT images of dogs. A total of 34,655 transverseimage slices obtained from 76 dogs with kidney calculi were used to develop the deep learning model. Because of the differences in kidney location and calculi sizes in dogs compared to humans, several processing methods were used. The first stage of the models, based on the Attention U-Net (AttUNet), was designed to detect the kidney for the coarse feature map. Five different models–AttUNet, UTNet, TransUNet, SwinUNet, and RBCANet–were used in the second stage to detect the calculi in the kidneys, and the performance of the models was evaluated. Compared with a previously developed model, all the models developed in this study yielded better dice similarity coefficients (DSCs) for the automatic segmentation of the kidney. To detect kidney calculi, RBCANet and SwinUNet yielded the best DSC, which was 0.74. In conclusion, the deep learning model developed in this study can be useful for the automated detection of kidney calculi.
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spelling pubmed-105486692023-10-05 A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs Ji, Yewon Hwang, Gyeongyeon Lee, Sang Jun Lee, Kichang Yoon, Hakyoung Front Vet Sci Veterinary Science Nephrolithiasis is one of the most common urinary disorders in dogs. Although a majority of kidney calculi are non-obstructive and are likely to be asymptomatic, they can lead to parenchymal loss and obstruction as they progress. Thus, early diagnosis of kidney calculi is important for patient monitoring and better prognosis. However, detecting kidney calculi and monitoring changes in the sizes of the calculi from computed tomography (CT) images is time-consuming for clinicians. This study, in a first of its kind, aims to develop a deep learning model for automatic kidney calculi detection using pre-contrast CT images of dogs. A total of 34,655 transverseimage slices obtained from 76 dogs with kidney calculi were used to develop the deep learning model. Because of the differences in kidney location and calculi sizes in dogs compared to humans, several processing methods were used. The first stage of the models, based on the Attention U-Net (AttUNet), was designed to detect the kidney for the coarse feature map. Five different models–AttUNet, UTNet, TransUNet, SwinUNet, and RBCANet–were used in the second stage to detect the calculi in the kidneys, and the performance of the models was evaluated. Compared with a previously developed model, all the models developed in this study yielded better dice similarity coefficients (DSCs) for the automatic segmentation of the kidney. To detect kidney calculi, RBCANet and SwinUNet yielded the best DSC, which was 0.74. In conclusion, the deep learning model developed in this study can be useful for the automated detection of kidney calculi. Frontiers Media S.A. 2023-09-20 /pmc/articles/PMC10548669/ /pubmed/37799401 http://dx.doi.org/10.3389/fvets.2023.1236579 Text en Copyright © 2023 Ji, Hwang, Lee, Lee and Yoon. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Veterinary Science
Ji, Yewon
Hwang, Gyeongyeon
Lee, Sang Jun
Lee, Kichang
Yoon, Hakyoung
A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
title A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
title_full A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
title_fullStr A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
title_full_unstemmed A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
title_short A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
title_sort deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs
topic Veterinary Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548669/
https://www.ncbi.nlm.nih.gov/pubmed/37799401
http://dx.doi.org/10.3389/fvets.2023.1236579
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