对喉声音的神经网络分析可以检测脑型狗的阻塞性上呼吸道疾病
Andrew McDonald1, Anurag Agarwal1, Ben Williams1
1Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
PloS one
|August 22, 2024
概括
一个新的AI模型可以使用呼吸声来检测和分级狗的胸头阻塞性气道综合征 (BOAS). 这项技术有助于早期诊断和治疗短脸品种这种常见的呼吸道疾病.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 人工智能的人工智能
- 呼吸系统生理学 呼吸系统生理学
背景情况:
- 胸头阻塞性气道综合征 (BOAS) 是短脸狗中普遍存在的呼吸道疾病,导致严重的健康问题.
- 临床征兆,如管,经常被忽视或低估,妨碍及时诊断和治疗.
- 目前对BOAS的分级方案需要经验丰富的从业人员,缺乏客观措施.
研究的目的:
- 开发和验证一个循环神经网络 (RNN) 模型,用于自动检测和分类狗中的斯特尔托.
- 引入一个客观的,人工智能驱动的工具来补充BOAS的现有临床评估.
- 为了提高BOAS诊断的准确性和可重复性.
主要方法:
- 从341只狗的665个喉电子听力镜记录的新型数据集进行了策划,这些狗的BOAS严重程度各不相同.
- 通过嵌套交叉验证,训练和评估了一个循环神经网络模型.
- 该模型的性能是根据其预测临床显著BOAS的能力进行评估的.
主要成果:
- RNN模型实现了0.85的曲线下的面积 (AUC),用于预测临床上显著的BOAS.
- 该模型显示操作灵敏度为71%和特异性为86%.
- 开发的算法显示在识别和分类stertor的高准确性,这是BOAS的一个关键标志.
结论:
- 人工智能驱动的驱动器检测模型为客观的BOAS评估提供了一个有前途的工具.
- 可以促进对BOAS的广泛查,使得更早的干预和改善患者的结果.
- 这项技术可以为受影响的狗种群提供更好的治疗和育种决策.
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