在怀孕期间进行非侵入性血液动力学分析的人工智能:开发一种新的评分系统
Nevardo Calderón-Restrepo1, Carolina Pemberthy-López1, Nazareth Campo-Campo2
1Escuela Ciencias de La Salud, Universidad Pontificia Bolivariana, Medellín, Colombia.
Pregnancy hypertension
|December 7, 2025
概括
一个新的AI-SCORE系统使用简单的临床数据准确地分类母亲的血液动力学概况. 这种非侵入性工具有助于在怀孕期间管理高血压疾病.
科学领域:
- 心血管生理学心血管生理学
- 孕产妇和胎儿医学 孕产妇和胎儿医学
- 医疗保健中的人工智能
背景情况:
- 血液动力学分析对于管理妊娠中高血压疾病至关重要,如产前.
- 目前的方法,如心声回声学是准确的,但昂贵,并不是广泛的可访问.
- 需要更简单的,非侵入性的工具来进行床边血液动力学评估.
研究的目的:
- 开发和验证一个人工智能驱动的评分系统 (AI-SCORE).
- 使用现有的临床变量对母亲的血液动力学概况进行分类.
- 为妊娠中血液动力学评估提供低成本,非侵入性的替代方案.
主要方法:
- 一项涉及90名低风险孕妇的横截面研究.
- 用于参考数据的心声学 (平均动脉压,心脏输出).
- 主要组件分析和K-means集群确定了血液动力学概况.
- 一个针对临床预测因素 (年龄,体重,心率,MAP/HR指数,妊娠三个月) 进行训练的一般回归神经网络创建了AI-SCORE.
主要成果:
- 确定了三种不同的血液动力学特征:高阻力 (36.7%),常态动力 (38.9%) 和高动力 (24.4%).
- 在区分个人资料方面,AI-SCORE表现良好 (AUC-ROC 0.706-0.811).
- 较低的AI-SCORE与高阻力/低动力概况相关;较高的得分表明了超动态状态.
结论:
- AI-SCORE可靠地对母亲的血液动力学表型进行了非侵入性分类.
- 使用低成本的临床输入来进行可访问的床边分析.
- 有潜力改善妊娠中高血压疾病的查,诊断和管理.
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