Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs

• Published in: PloS one Vol. 19; no. 8; p. e0305633
• Main Authors: McDonald, Andrew, Agarwal, Anurag, Williams, Ben, Liu, Nai-Chieh, Ladlow, Jane
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.08.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Acoustic properties; Airway Obstruction - diagnosis; Airway Obstruction - physiopathology; Airway Obstruction - veterinary; Algorithms; Animals; Auscultation; Biology and Life Sciences; Cardiac stress tests; Computer and Information Sciences; Craniosynostoses - diagnosis; Craniosynostoses - physiopathology; Craniosynostoses - veterinary; Cyanosis; Datasets; Diagnosis; Disease; Diseases; Dog Diseases - diagnosis; Dog Diseases - physiopathology; Dogs; Female; Health aspects; Life span; Machine learning; Male; Medical instruments; Medicine and Health Sciences; Morbidity; Network analysis; Neural networks; Neural Networks, Computer; Noise; People and Places; Pharynx; Pharynx - physiology; Pharynx - physiopathology; Physical Sciences; Recurrent neural networks; Research and Analysis Methods; Respiratory diseases; Respiratory organs; Respiratory Sounds - diagnosis; Respiratory Sounds - physiopathology; Respiratory tract diseases; Sensitivity analysis; Signal processing; Sleep disorders; Sound; Sounds; Throat; Veterinary medicine; France; United Kingdom > UK
• DOI: 10.1371/journal.pone.0305633
• ISSN: 1932-6203

Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affecting popular short-faced dog breeds such as Pugs and French bulldogs. BOAS causes significant morbidity, leading to poor exercise tolerance, sleep disorders and a shortened lifespan. Despite its severity, the disease is commonly missed by owners or disregarded by veterinary practitioners. A key clinical sign of BOAS is stertor, a low-frequency snoring sound. In recent years, a functional grading scheme has been introduced to semi-objectively grade BOAS based on the presence of stertor and other abnormal signs. However, correctly grading stertor requires significant experience and adding an objective component would aid accuracy and repeatability. This study proposes a recurrent neural network model to automatically detect and grade stertor in laryngeal electronic stethoscope recordings. The model is developed using a novel dataset of 665 labelled recordings taken from 341 dogs with diverse BOAS clinical signs. Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.