Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Institute of Veterinary Clinical Science, School of Veterinary Medicine, National Taiwan University, Taipei, Taiwan.
PLoS One. 2024 Aug 22;19(8):e0305633. doi: 10.1371/journal.pone.0305633. eCollection 2024.
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.
短头气道阻塞综合征（BOAS）是一种高度流行的呼吸道疾病，影响到一些常见的短面犬种，如哈巴狗和法国斗牛犬。BOAS 会导致严重的发病率，导致运动耐量差、睡眠障碍和寿命缩短。尽管这种疾病很严重，但它常常被主人忽视或被兽医忽视。BOAS 的一个关键临床特征是喘鸣，一种低频的呼噜声。近年来，已经引入了一种功能分级方案，根据喘鸣和其他异常体征，对 BOAS 进行半客观分级。然而，正确分级喘鸣需要丰富的经验，添加客观成分将有助于提高准确性和可重复性。本研究提出了一种基于递归神经网络模型，自动检测和分级喉电子听诊器记录中的喘鸣。该模型是使用一个新的数据集开发的，该数据集包含 341 只具有不同 BOAS 临床症状的犬的 665 个标记记录。通过嵌套交叉验证进行评估，神经网络预测具有临床意义的 BOAS 的存在，其接收者操作特征曲线下面积为 0.85，操作灵敏度为 71%，特异性为 86%。该算法可以使主人和兽医都能够广泛地进行 BOAS 筛查，从而改善治疗和繁殖决策。
