Deep learning-based evaluation of the severity of mitral regurgitation in canine myxomatous mitral valve disease patients using digital stethoscope recordings
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.
Authors
- Kyoung‐Won Seo (ORCID: https://orcid.org/0000-0002-1561-3278)
- Hao‐Chih Chang (ORCID: https://orcid.org/0000-0002-6256-2194)
- Min‐Ok Ryu (ORCID: https://orcid.org/0000-0002-5614-9846)
- Se-Hoon Kim
- Hwa-Young Yoon (ORCID: https://orcid.org/0000-0002-5472-3664)
- Jihye Choi
- S H Lee (ORCID: https://orcid.org/0009-0005-4511-038X)
- Sang Eun Lee (ORCID: https://orcid.org/0000-0003-4720-9955)
- Won-Yang Cho
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Korean Society of Medical Ultrasound (KR)
Publication Details
- Journal
- BMC Veterinary Research
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1186/s12917-025-04802-z
- Citations
- 2
- Primary Topic
- Phonocardiography and Auscultation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- Seoul National University
- Ministry of SMEs and Startups
- Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University