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Deep learning-based diagnosis of feline hypertrophic cardiomyopathy

Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10–15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the go...

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Autores principales: Rho, Jinhyung, Shin, Sung-Min, Jhang, Kyoungsun, Lee, Gwanghee, Song, Keun-Ho, Shin, Hyunguk, Na, Kiwon, Kwon, Hyo-Jung, Son, Hwa-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894403/
https://www.ncbi.nlm.nih.gov/pubmed/36730319
http://dx.doi.org/10.1371/journal.pone.0280438
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author Rho, Jinhyung
Shin, Sung-Min
Jhang, Kyoungsun
Lee, Gwanghee
Song, Keun-Ho
Shin, Hyunguk
Na, Kiwon
Kwon, Hyo-Jung
Son, Hwa-Young
author_facet Rho, Jinhyung
Shin, Sung-Min
Jhang, Kyoungsun
Lee, Gwanghee
Song, Keun-Ho
Shin, Hyunguk
Na, Kiwon
Kwon, Hyo-Jung
Son, Hwa-Young
author_sort Rho, Jinhyung
collection PubMed
description Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10–15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the gold standards in the diagnosis of feline HCM. However, only 75% accuracy has been achieved using radiography alone. Therefore, we trained five residual architectures (ResNet50V2, ResNet152, InceptionResNetV2, MobileNetV2, and Xception) using 231 ventrodorsal radiographic images of cats (143 HCM and 88 normal) and investigated the optimal architecture for diagnosing feline HCM through radiography. To ensure the generalizability of the data, the x-ray images were obtained from 5 independent institutions. In addition, 42 images were used in the test. The test data were divided into two; 22 radiographic images were used in prediction analysis and 20 radiographic images of cats were used in the evaluation of the peeking phenomenon and the voting strategy. As a result, all models showed > 90% accuracy; Resnet50V2: 95.45%; Resnet152: 95.45; InceptionResNetV2: 95.45%; MobileNetV2: 95.45% and Xception: 95.45. In addition, two voting strategies were applied to the five CNN models; softmax and majority voting. As a result, the softmax voting strategy achieved 95% accuracy in combined test data. Our findings demonstrate that an automated deep-learning system using a residual architecture can assist veterinary radiologists in screening HCM.
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spelling pubmed-98944032023-02-03 Deep learning-based diagnosis of feline hypertrophic cardiomyopathy Rho, Jinhyung Shin, Sung-Min Jhang, Kyoungsun Lee, Gwanghee Song, Keun-Ho Shin, Hyunguk Na, Kiwon Kwon, Hyo-Jung Son, Hwa-Young PLoS One Research Article Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10–15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods and indices, radiography and ultrasound are the gold standards in the diagnosis of feline HCM. However, only 75% accuracy has been achieved using radiography alone. Therefore, we trained five residual architectures (ResNet50V2, ResNet152, InceptionResNetV2, MobileNetV2, and Xception) using 231 ventrodorsal radiographic images of cats (143 HCM and 88 normal) and investigated the optimal architecture for diagnosing feline HCM through radiography. To ensure the generalizability of the data, the x-ray images were obtained from 5 independent institutions. In addition, 42 images were used in the test. The test data were divided into two; 22 radiographic images were used in prediction analysis and 20 radiographic images of cats were used in the evaluation of the peeking phenomenon and the voting strategy. As a result, all models showed > 90% accuracy; Resnet50V2: 95.45%; Resnet152: 95.45; InceptionResNetV2: 95.45%; MobileNetV2: 95.45% and Xception: 95.45. In addition, two voting strategies were applied to the five CNN models; softmax and majority voting. As a result, the softmax voting strategy achieved 95% accuracy in combined test data. Our findings demonstrate that an automated deep-learning system using a residual architecture can assist veterinary radiologists in screening HCM. Public Library of Science 2023-02-02 /pmc/articles/PMC9894403/ /pubmed/36730319 http://dx.doi.org/10.1371/journal.pone.0280438 Text en © 2023 Rho et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rho, Jinhyung
Shin, Sung-Min
Jhang, Kyoungsun
Lee, Gwanghee
Song, Keun-Ho
Shin, Hyunguk
Na, Kiwon
Kwon, Hyo-Jung
Son, Hwa-Young
Deep learning-based diagnosis of feline hypertrophic cardiomyopathy
title Deep learning-based diagnosis of feline hypertrophic cardiomyopathy
title_full Deep learning-based diagnosis of feline hypertrophic cardiomyopathy
title_fullStr Deep learning-based diagnosis of feline hypertrophic cardiomyopathy
title_full_unstemmed Deep learning-based diagnosis of feline hypertrophic cardiomyopathy
title_short Deep learning-based diagnosis of feline hypertrophic cardiomyopathy
title_sort deep learning-based diagnosis of feline hypertrophic cardiomyopathy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894403/
https://www.ncbi.nlm.nih.gov/pubmed/36730319
http://dx.doi.org/10.1371/journal.pone.0280438
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