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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10548669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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