开发一种新型的人工智能算法,用于解释胎儿心率和子宫活动数据,用于心脏图解
Rohit Pardasani1, Renee Vitullo1, Sara Harris1
1GE HealthCare, Chicago, IL, United States.
Frontiers in digital health
|October 2, 2025
概括
开发了一种新的人工智能 (AI) 算法来分析胎儿心率 (FHR) 和子宫活动 (UA) 追踪. 这种人工智能工具旨在提高分娩期间心脏图谱 (CTG) 解释的一致性和准确性.
科学领域:
- 产周医学 产周医学
- 医疗人工智能的人工智能
- 在医疗保健中的信号处理.
背景情况:
- 心电图 (CTG) 对于在分娩期间评估胎儿健康至关重要,测量胎儿心率 (FHR) 和子宫活动 (UA).
- 手动解释CTG跟踪是主观的,可能导致临床决策的变化.
- 人工智能 (AI) 提供了一个潜在的解决方案,用于对复杂的生理数据进行客观和一致的分析.
研究的目的:
- 开发和训练一种新的AI算法,用于自动分析和解释心电图 (CTG) 数据.
- 通过提供对胎儿追踪的一致和全面的解释来支持临床决策.
- 利用深度学习和基于规则的技术来识别FHR和UA信号中的关键事件.
主要方法:
- 使用FHR和UA输入设计了一个具有三平行一维Unet架构的深度学习模型.
- 该算法是在一个大数据集上训练的,其中包括1600个标记的胎儿跟踪.
- 绩效评估包括回忆,精度,F1得分,并将预测的基线准确性与临床医生的解释进行比较.
主要成果:
- 在测试组中,人工智能算法获得了F1分数,加速为0.803,减速为0.520,收缩为0.868.
- 与专家临床医生的解释相比,观察到高预测基线准确率为91.5% (在≤5bpm范围内).
- 该模型成功地确定了胎儿跟踪中感兴趣的关键细分.
结论:
- 一种用于分析胎儿心率和子宫活动数据的新型人工智能算法已成功开发和训练.
- 人工智能展示了通过协助临床医生在床边心脏图解解释来增强患者护理的潜力.
- 这项技术可能会导致在分娩期间对胎儿健康状况进行更一致和客观的评估.
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