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预测早产:过去,现在和未来的方法来应对持续的挑战

Christine Henricks¹, David Nelson¹

¹Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9032, USA; Parkland Health 5200 Harry Hines Blvd, Dallas, TX 75235, USA.

Seminars in perinatology

|December 6, 2025

概括

预测早产仍然具有挑战性,因为传统方法缺乏准确性. 像人工智能和多omics这样的新兴技术有望改善预测,但需要在不同的人口中进行验证.

科学领域:

围产期健康围产期健康
生殖医学是一种生殖医学.
基因组学和蛋白质组学

背景情况:

在全球范围内,早产是新生儿发病率和死亡率的主要原因,尽管进行了广泛的研究,但其比例基本没有变化.
传统的预测方法 (风险评分,宫长度,胎儿纤维素) 的准确性有限.
迫切需要先进的预测和预防策略来预防早产.

研究的目的:

审查用于早产预测的新兴技术.
突出多态学和人工智能在提高预测准确性的潜力.
讨论临床翻译的挑战和未来方向.

主要方法:

对蛋白质组,代谢组和遗传学研究的审查.
应用人工智能 (AI) 和机器学习 (ML) 来整合多omics和临床数据.
对局限性的分析,包括异构的定义,小样本大小和缺乏多样化的验证.

主要成果:

蛋白质组学确定了炎症和血管生成途径;代谢组学揭示了生化和微生物的变化.
遗传研究表明,复杂的母体和胎儿基因组贡献.
早期的AI/ML研究表明,与传统模型相比,预测准确度有所提高,但挑战仍然存在.

结论:

新兴技术,特别是集成多omics数据的AI,为早产预测提供了新的途径.
仍然存在重大挑战,包括研究异质性,跨不同人群的验证以及解决健康的社会决定因素.
持续的创新,纵向研究和对公平的关注对于临床转换和改善围产期结果至关重要.

关键词:

人工智能的人工智能是人工智能.生物标志物生物标志物宫的长度宫的长度胎儿纤维生殖素是什么基因组学就是基因组学.在家进行子宫活动监测.机器学习是机器学习.代谢学代谢学代谢学预测预测预测过早分娩过早分娩是什么蛋白质组学是指蛋白质组学.

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