Peer-reviewed veterinary case report
Machine learning to assess breathing sounds in brachycephalic dogs
By Oren, Ariel et al.·Published in Scientific reports·2023·Information Systems Department·View original on PubMed →
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Original publication title: BrachySound: machine learning based assessment of respiratory sounds in dogs.
- Species:
- dog
Plain-English summary
A study looked at how machine learning can help diagnose breathing problems in dogs, specifically brachycephalic obstructive airway syndrome (BOAS), which is common in breeds like Pugs. Traditional methods of checking for this condition can be slow and depend on the vet's experience, but this new approach uses audio recordings of dogs' breathing sounds during exercise to make a more accurate diagnosis. The machine learning models showed promising results, correctly identifying affected dogs with high accuracy. This could lead to faster and more reliable assessments for dogs with breathing issues, improving their overall care.
People also search for: dog breathing problems · Pug BOAS diagnosis · machine learning in veterinary care
Abstract
The early and accurate diagnosis of brachycephalic obstructive airway syndrome (BOAS) in dogs is pivotal for effective treatment and enhanced canine well-being. Owners often do underestimate the severity of BOAS in their dogs. In addition, traditional diagnostic methods, which include pharyngolaryngeal auscultation, are often compromised by subjectivity, are time-intensive and depend on the veterinary surgeon's experience. Hence, new fast, reliable assessment methods for BOAS are required. The aim of the current study was to use machine learning techniques to bridge this scientific gap. In this study, machine learning models were employed to objectively analyze 366 audio samples from 69 Pugs and 79 other brachycephalic breeds, recorded with an electronic stethoscope during a 15-min standardized exercise test. In classifying the BOAS test results as to whether the dog is affected or not, our models achieved a peak accuracy of 0.85, using subsets from the Pugs dataset. For predictions of the BOAS results from recordings at rest in Pugs and various brachycephalic breeds, accuracies of 0.68 and 0.65 were observed, respectively. Notably, the detection of laryngeal sounds achieved an F1 score of 0.80. These results highlight the potential of machine learning models to significantly streamline the examination process, offering a more objective assessment than traditional methods. This research indicates a turning point towards a data-driven, objective, and efficient approach in canine health assessment, fostering standardized and objective BOAS diagnostics.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/37985864/