ArticleFrontiers in veterinary science2023
Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs.
Article in Frontiers in veterinary science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Development of an artificial intelligence-based algorithm for the detection of left atrial enlargement from feline thoracic radiographs.The veterinary quarterly · 2026Article
- Bibliometric Analysis of Artificial Intelligence Use in Small Animal Thoracic Radiography.Veterinary medicine and science · 2026Article
- Pre-anaesthetic risk assessment and management of dogs with myxomatous mitral valve disease: a spectrum of care narrative review.The Journal of small animal practice · 2026Review
- Canine radiography and echocardiography sensor fusion for deep learning-based detection and classification of myxomatous mitral valve disease.Journal of thoracic disease · 2026Article
- Automated segmentation of canine pulmonary masses in CT imaging using AI.The veterinary quarterly · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Vertebral Heart Score and Vertebral Left Atrial Size as Radiographic Measurements for Cardiac Size in Dogs-A Literature Review.Animals : an open access journal from MDPI · 2025Review
- Automated AI-based segmentation of canine hepatic focal lesions from CT studies.Frontiers in veterinary science · 2025Article
- Machine Learning Techniques for Canine Myxomatous Mitral Valve Disease Classification: Integrating Anamnesis, Quality of Life Survey, and Physical Examination.Veterinary sciences · 2024Article
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Authors and funding
8 authors.
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Abstract
An algorithm based on artificial intelligence (AI) was developed and tested to classify different stages of myxomatous mitral valve disease (MMVD) from canine thoracic radiographs. The radiographs were selected from the medical databases of two different institutions, considering dogs over 6 years of age that had undergone chest X-ray and echocardiographic examination. Only radiographs clearly showing the cardiac silhouette were considered. The convolutional neural network (CNN) was trained on both the right and left lateral and/or ventro-dorsal or dorso-ventral views. Each dog was classified according to the American College of Veterinary Internal Medicine (ACVIM) guidelines as stage B1, B2 or C + D. ResNet18 CNN was used as a classification network, and the results were evaluated using confusion matrices, receiver operating characteristic curves, and t-SNE and UMAP projections. The area under the curve (AUC) showed good heart-CNN performance in determining the MMVD stage from the lateral views with an AUC of 0.87, 0.77, and 0.88 for stages B1, B2, and C + D, respectively. The high accuracy of the algorithm in predicting the MMVD stage suggests that it could stand as a useful support tool in the interpretation of canine thoracic radiographs.
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