使用深度学习技术预测分娩期间胎儿血压
John Tolladay1, Christopher A Lear2, Laura Bennet2
1Oxford Labour Monitoring Group, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, OX1 2JD, UK.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
研究人员开发了一个模型来预测胎儿心率 (FHR) 减速期间的最低胎儿血压 (FBP),使用在羊数据上训练的卷积神经网络来预测. 虽然有希望,但需要进一步开发才能准确地预测人类FBP.
科学领域:
- 产周医学 产周医学
- 计算生物学是一种计算生物学.
- 医疗保健中的人工智能
背景情况:
- 胎儿心率 (FHR) 监测对于评估胎儿健康至关重要.
- 在FHR减速期间的胎儿血压 (FBP) 变化可能表明妥协.
- 在减速过程中准确预测FBP仍然具有挑战性.
研究的目的:
- 开发一个预测模型,用于胎儿心率减速期间的最低胎儿血压 (FBP).
- 使用卷积神经网络 (CNN) 来估计FBP.
- 评估模型在人类FHR数据上的性能.
主要方法:
- 一个CNN被训练使用实验数据从2698塞在近期的胎儿羊.
- 经过训练的模型估计了FBP从53,445个人类FHR信号的减速.
- 使用平均绝对误差,平均绝对百分比误差和R平方来评估性能.
主要成果:
- 该模型预测了绵羊FBP的最低值,平均绝对误差为6.7mmHg (R2=0.36).
- 人类FHR数据的表现不足以清楚地预测出生时的严重妥协.
- 该模型准确地预测了一些绵羊减速的FBP,但在极端的FBP值方面遇到了困难.
结论:
- 开发的CNN显示了在FHR减速期间预测人类FBP的潜力.
- 需要进一步开发更大,更多样化的数据集以提高准确性.
- 该模型目前的局限性突显出需要继续研究人工智能驱动的胎儿监测.
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