机器学习区分右后部与右前部辅助通路,使用6导电心电图在患有心室预兴奋的狗中
Wyatt H Flanders1, N Sydney Moïse1, Roberto Santilli1,2
11Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY.
Journal of the American Veterinary Medical Association
|November 12, 2025
概括
一个机器学习模型准确地识别了犬腔室预兴奋 (VPE) 辅助路径. 这种人工智能工具精确地定位后部与前部通路,改善VPE诊断.
科学领域:
- 兽医心脏病学 兽医心脏病学
- 人工智能在医学中的应用
- 生物医学信号处理
背景情况:
- 狗的心室预兴奋 (VPE) 涉及附属通路,其局部化可能具有挑战性.
- 精确地定位这些通路对于有效的诊断和治疗至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于识别狗心电图 (ECG) 上的信任点.
- 目标是在VPE期间准确地定位右侧辅助通路作为后部或前部.
主要方法:
- 用一维U-net算法预处理了患有VPE的狗的心电图记录.
- 该ML模型在3,405次节拍中进行了训练,并在1,984次节拍中进行了测试.
- 验证涉及向量心脏图和平均电轴 (MEA) 计算,使用接收器操作员特征曲线 (AUC) 评估性能.
主要成果:
- 该ML模型在识别ECG关键信任点方面取得了很高的准确性 (P波到三角波:AUC,0.957;三角波/QRS:AUC,0.965).
- 它准确地将节拍分类为来自后部 (AUC,0.917) 或前部 (AUC,0.948) 路径的节拍.
- 该模型确定了82%的试验犬的正确通路位置,MEA分析证实后部和前部通路之间存在显著差异 (P < .01).
结论:
- 开发的ML模型在定位犬类VPE的辅助途径方面表现出高准确度.
- 这种人工智能驱动的方法显示出在兽医中推进VPE诊断能力的巨大潜力.
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