Scholarlink

<p>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&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning-driven automated detection of canine cardiac murmurs via digital wireless stethoscope auscultation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

查看全文
  • Sully Lee,
  • HyeSun Chang,
  • Won-Yang Cho,
  • Soyeon Jeon,
  • Sangjun Lee,
  • Sehoon Kim,
  • Min-Ok Ryu,
  • Kyoung-Won Seo

摘要

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.