Evaluation of the effectiveness of two neural networks (Resnet50v2 and Inception V3) in identifying left atrial enlargement in adult dogs.
DOI:
https://doi.org/10.5380/avs.v29i3.95142Keywords:
Artificial intelligence, CNN, Heart, Radiology, Enlarged left atriumAbstract
Mitral valve endocardiosis is a heart disease that is present in approximately 75% of cases of canine heart disease. Currently, the gold standard for diagnosis is echocardiography, but radiography provides us with valuable information for clinical staging of the disease, such as detecting left atrial enlargement, the presence or absence of pulmonary congestion and cardiogenic pulmonary edema more quickly. The aim of this study was to evaluate the effectiveness of two neural networks, Resnet50v2 and Inception V3, in detecting left atrial enlargement. A database was built with 1052 lateral images. The images were then separated into three distinct groups: 198 images with left atrial enlargement (LAA), 124 images with left atrial enlargement simultaneously with pulmonary edema (LAA_EP), totaling 322 images with left atrial enlargement and 580 images considered normal (N). As a result, all the models were > 90% accurate; Resnet50V2: 94.77%; Inception V3: 92.84%. In conclusion, the use of artificial neural networks to classify left atrial enlargement in radiographs of dogs is a viable approach that can help veterinary radiologists screen patients for left atrial enlargement.
References
Banzato T, Bonsembiante F, Aresu L, Gelain ME, Burti S & Zotti A. Use of transfer learning to detect diffuse degenerative hepatic diseases from ultrasound images in dogs: a methodological study. The Veterinary Journal. 233:35–40, 2018. doi: 10.1016/j.tvjl.2017.12.026(a)
Banzato T, Cherubini GB, Atzori M. & Zotti A. Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images. The Veterinary Journal. 235:90–2, 2018. doi: 10.1016/j.tvjl.2018.04.001 (b)
Banzato T, Causin F, Puppa A.D, Cester G, Mazzai L & Zotti A. Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study. Journal of Magnetic Resonance Imaging, 2019. 50:1152–9. doi: 10.1002/jmri.26723
Banzato T, Wodzinski M, Tauceri F, Donà C, Scavazza F, Müller H. & Zotti A. An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats. Frontiers in Veterinary Science, 2021. Oct 15(8):731936. doi: 10.3389/fvets.2021.731936.
Boissady E, De La Comble A, Zhu X, Abbott J & Adrien-Maxence H. Comparison of a Deep Learning Algorithm vs. Humans for Vertebral Heart Scale Measurements in Cats and Dogs Shows a High Degree of Agreement Among Readers. Frontiers in Veterinary Science, 2021. Dec 9(8):764570. doi: 10.3389/fvets.2021.764570.
Borgarelli M & Buchanan JW. Historical review, epidemiology and natural history of degenerative mitral valve disease. Journal of Veterinary Cardiology. 14:93-101, 2012. Doi.org/10.1016/j.jvc.2012.01.011.
Boswood A, Häggström J, Gordon SG, Wess G, et al., Effect of Pimobendan in Dogs with Preclinical Myxomatous Mitral Valve Disease and Cardiomegaly: The EPIC Study—A Randomized Clinical Trial. Journal of Veterinary Internal Medicine. 30: 1765-1779 2016. https://doi.org/10.1111/jvim.14586
Buchanan JW. & Bucheler J. Vertebral heart size standards in dogs: attempting to differentiate normal versus abnormal cardiomegaly. Journal of Veterinary Internal Medicine. 9(4):224–230, 1995.
Burti S. Osti LV, Zotti A & BANZATO T. Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs. The Veterinary Journal. 262:1-7, 2020. https://doi.org/10.1016/j.tvjl.2020.105505
Chassagnona G, Vakalopoulou M, Paragios N & Revel MP. Artificial intelligence applications for thoracic imaging. European Journal of Radiology. 123:18774, 2020. doi: 10.1016/j.ejrad.2019.108774
Garcia ML. & Maciel NF. Inteligência artificial no acesso a saúde: Reflexões sobre a utilização da telemedicina em tempos de pandemia. Revista Eletrônica Direito e Política. 15:2, n.2, 2023. https://siaiap32.univali.br/seer/index.php/rdp/article/view/16866/9581.
Haykin S. Neural Networks and Learning Machines. [S.l.: s.n.], 2008. v. 3, p. 906, 2020.
Hicks SA, Strumke I, Thambawita V, Hammou M, Riegler M.A, Halvorsen P & Parasa S. On evaluation metrics for medical applications of artificial intelligence. Scientific Reports. 12:5979, 2022.
Keene BW, Atkins CE, Bonagura JD, Fox PR, Häggström J, Fuentes VL, Oyama MA, Rush JE, Stepien R & Uechi M. ACVIM consensus guidelines for the diagnosis and treatment of myxomatous mitral valve disease in dogs. Journal of Veterinary Internal Medicine. 33(3):1127-1140, 2019. doi: 10.1111/jvim.15488.
Kim E, Fischetti AJ, Sreetharan P, Weltman JG. & Fox PR. Comparison of artificial intelligence to the veterinary radiologist's diagnosis of canine cardiogenic pulmonary edema. Veterinary Radiology & Ultrasound. 63(3): 292-297, 2022. doi: 10.1111/vru.13062.
Lam C, Gavaghan BJ. & Meyers FE. Radiographic quantification of left atrial size in dogs with myxomatous mitral valve disease. Journal of Veterinary Internal Medicine. 35(2): 747-754, 2021. doi: 10.1111/jvim.16073.
Lecun Y, Bengio Y & Hinton G. Deep learning. Nature. 521:436–444, 2015. https://doi.org/10.1038/nature14539
Li S, Wang Z, Visser LC, Wisner ER. & Cheng H. Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Veterinary Radiology & Ultrasound. 61(6): 611-618, 2020. doi: 10.1111/vru.12901.
Malcolm EL., Visser LC., Phillips KL. & Johnson LR. Diagnostic value of vertebral left atrial size as determined from thoracic radiographs for assessment of left atrial size in dogs with myxomatous mitral valve disease. Journal of American Veterinary Medical Association. 253: 1038–1045, 2018. doi: 10.2460/javma.253.8.1038.
Mongan J, Moy L & Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence. 2(2), 2020. https://doi.org/10.1148/ryai.2020200029.
Rahmani H, Khan S, Shah SAA. & Bennamoun M. A Guide to Convolutional Neural Networks for Computer Vision. 1. ed. [S.l.]: Morgan & Claypool. P. 207, 2018.
Solomon J, Bender S, Durgempudi P, Robar C, Cocchiaro M, Turner S, Watson C, Healy J, Spake J & Szlosek D. Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs. Journal of Small Animal Practice. 64(12): 769-775, 2023. doi: 10.1111/jsap.13666.
Valente C, Wodzinski M, Guglielmini C, Poser H, Chiavegato D, Zotti A, Venturini R, & Banzato T. Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs. Frontiers in Veterinary Science. v.10, 2023. doi: 10.3389/fvets.2023.1227009
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