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

## TL;DR

A deep learning model was developed to detect heart murmurs in dogs using a digital stethoscope, achieving accuracy comparable to experienced veterinarians.

## Contribution

A novel CNN-based algorithm (CNN6) for automated canine heart murmur detection using PCG data from wireless stethoscopes.

## Key findings

- The model achieved 89.9% sensitivity and 92.7% specificity in detecting heart murmurs.
- Performance was validated using 2,269 recordings from 406 dogs and an independent test set of 297 recordings from 60 dogs.
- The AI tool is suitable for screening myxomatous mitral valve disease and supports telemedicine in veterinary cardiology.

## 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.