Peer-reviewed veterinary case report
Using deep learning to grade mitral valve leakage in dogs with heart
By Lee, Soh-Yeon et al.·Published in BMC veterinary research·2025·Department of Veterinary Clinical Science, South Korea·View original on PubMed →
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Original publication title: Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings.
- Species:
- dog
Plain-English summary
A group of dogs with myxomatous mitral valve disease (MMVD), a common heart problem, were evaluated for the severity of mitral regurgitation (MR), which can lead to heart failure. Researchers used digital stethoscope recordings and a deep learning model called CNN6 to analyze the heart sounds, achieving over 94% accuracy in determining how severe the MR was. This method could provide a quick and noninvasive way for vets to assess heart issues in dogs, complementing traditional echocardiography. The study suggests that this technology could improve early diagnosis and treatment for affected dogs.
People also search for: dog heart murmur treatment · myxomatous mitral valve disease in dogs · digital stethoscope for dog heart problems
Abstract
BACKGROUND: Myxomatous mitral valve disease (MMVD) represents the most prevalent cardiac disorder in dogs, frequently resulting in mitral regurgitation (MR) and congestive heart failure. Although echocardiography is the gold standard for diagnosis, it is an expensive tool that involves significant clinical training to ensure consistent application. Deep learning models offer an innovative approach to assessing MR using digital stethoscopic recordings, enabling early screening and precise prediction. Thus, in this study, we evaluated the effectiveness of a convolutional neural network 6 (CNN6) in providing an objective alternative to traditional methods for assessing MR. This study, conducted at the Seoul National University Veterinary Medicine Teaching Hospital, included 460 dogs with MMVD, classified according to the American College of Veterinary Internal Medicine guidelines. Phonocardiogram signals were recorded using digital stethoscopes and analyzed using the deep models CNN6, patch-mix audio spectrogram transformer (PaSST), and residual neural network (ResNET38), which were trained to categorize MR severity into mild, moderate, and severe based on MINE score. Performance metrics were calculated to evaluate model effectiveness. RESULTS: The CNN6-Fbank model achieved an accuracy of 94.12% [95% confidence interval (CI): 94.11-93.12], specificity of 97.30% (95% CI: 97.30-97.34), sensitivity of 94.12% (95% CI: 93.74-94.50), precision of 92.63% (95% CI: 92.29-92.97), and F1 score of 93.32% (95% CI: 93.05-93.59), outperforming the PaSST and ResNet38 models overall and demonstrating robust performance across most metrics. CONCLUSIONS: Deep learning models, particularly CNN6, can effectively assess MR severity in dogs with MMVD using digital stethoscope recordings. This approach provides a rapid, noninvasive, and reliable adjunct to echocardiography, potentially enhancing diagnosis and outcomes. Future studies should focus on broader clinical validation and real-time application of this technology.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/40336065/