一个机器学习算法来分类心脏声和阶段临床前myxomatous中枢门疾病在狗中
Andrew McDonald1, Jose Novo Matos2, Joel Silva2,3
1Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Journal of veterinary internal medicine
|October 21, 2024
概括
一个机器学习算法可以准确地检测和分级狗的心脏声,有助于早期识别心脏病,如状心肌膜疾病. 该工具为可访问的兽医查提供了一个有希望的解决方案.
科学领域:
- 兽医心脏病学 兽医心脏病学
- 动物健康中的人工智能
- 生物医学信号处理
背景情况:
- 狗的心脏声是心脏病的关键指标,特别是心肌膜病 (MMVD).
- 准确地解释狗的心声需要专门的兽医专业知识.
- 早期检测和分类声对于及时干预和管理犬类心脏病至关重要.
研究的目的:
- 为了评估机器学习算法在使用电子耳语镜记录来检测和分类狗的心脏声时的有效性.
- 评估该算法的识别心脏病的能力,特别是区分临床前myxomatous中枢门疾病阶段.
- 为了确定人类训练的算法是否可以适应犬类心脏听觉.
主要方法:
- 一个反复的神经网络算法,最初是为人类心脏声检测而开发的,使用756只狗的数据集进行了微调.
- 狗接受了兽医心脏病学家的综合体质和心声学检查,以对声进行分级和诊断疾病.
- 该算法分析了电子听力镜音频记录,以预测声等级,并区分临床前MMVD阶段.
主要成果:
- 该算法在检测任何级别的声中实现了87.9%的灵敏度和81.7%的特异性.
- 在57.0%的病例中,算法预测的声等级和心脏病学家的等级之间确切一致.
- 该算法有效地区分了临床前MMVD阶段 (B1与B2),AUC为0.861,对大声的敏感度为81.4%,特异性为73.9%.
结论:
- 一个机器学习算法可以成功地从人类数据中进行调整,以准确分类狗的心脏声.
- 该模型有望帮助区分临床前的myxomatous米特拉病.
- 这项技术为初级兽医护理机构提供了准确,经济高效的心脏查的潜力.
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