ArticleVeterinary research communications2026
Deep learning-driven automated detection of canine cardiac murmurs via digital wireless stethoscope auscultation.
Article in Veterinary research communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Canine radiography and echocardiography sensor fusion for deep learning-based detection and classification of myxomatous mitral valve disease.Journal of thoracic disease · 2026Article
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Authors and funding
8 authors.
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Abstract
A heart murmur is a key indicator of cardiovascular disease, making auscultation essential. Nonetheless, its accuracy is influenced by the clinician’s experience and subjective interpretation. This study developed a deep learning–based algorithm (CNN6) for automated detection of heart murmurs using phonocardiogram (PCG) data acquired via a digital wireless stethoscope. A total of 2,269 recordings (over 20 h) from 406 dogs were used for model development and validation, and 297 recordings from 60 dogs were reserved for independent testing. The model achieved 89.9% sensitivity, 92.7% specificity, and 90.9% accuracy, demonstrating diagnostic performance comparable to that of experienced veterinarians. This AI-assisted approach provides a consistent and objective murmur assessment and represents a clinically applicable screening tool for myxomatous mitral valve disease (MMVD), enhancing diagnostic precision, facilitating telemedicine, and promoting the integration of artificial intelligence into veterinary cardiology.
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