BrachySound:基于机器学习的评估,对狗的呼吸声进行评估
Ariel Oren1, Jana D Türkcü2, Sebastian Meller2
1Information Systems Department, University of Haifa, Haifa, Israel.
Scientific reports
|November 21, 2023
概括
机器学习使用音频录音准确地诊断狗的胸头阻塞性呼吸道综合征 (BOAS). 这为改善犬的健康提供了一个更快,客观的替代传统兽医评估.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 机器学习 机器学习
- 生物声学是一种生物声学.
背景情况:
- 甲脑阻塞性呼吸道综合征 (BOAS) 的诊断是主观的,需要大量的时间.
- 目前对狗BOAS的诊断方法缺乏客观性和效率.
- 准确的BOAS诊断对于有效的治疗和狗的福利至关重要.
研究的目的:
- 开发和验证机器学习模型,用于对狗的客观BOAS诊断.
- 评估AI在分析犬的呼吸声中对BOAS检测的有效性.
- 为了提供一个更快,更可靠的诊断工具,为脑性品种.
主要方法:
- 使用电子耳语镜分析了366个马和其他脑性品种的音频录音.
- 用标准化的15分钟运动测试来捕捉呼吸声.
- 机器学习模型在音频数据上进行了训练和测试,用于BOAS分类.
主要成果:
- 在使用特定模型子集对马车的BOAS进行分类时,达到0.85的峰值精度.
- 预测马和其他品种的休息BOAS的准确度分别为0.68和0.65.
- 检测到暗示BOAS的喉声音,获得了F1得分0.80.
结论:
- 机器学习为狗BOAS诊断提供了一种有希望的客观方法.
- 人工智能驱动的分析可以显著简化BOAS的兽医检查过程.
- 这种数据驱动的方法代表了向对狗进行标准化和高效的BOAS评估的转变.
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