人工智能在重复的怀孕损失:当前的证据,局限性和未来的方向
Athanasios Zikopoulos1, Efthalia Moustakli2, Anastasios Potiris3
1Torbay and South Devon NHS Foundation Trust, Lowes Brg, Torquay TQ2 7AA, UK.
Journal of clinical medicine
|January 28, 2026
概括
人工智能 (AI) 和机器学习 (ML) 可以分析复杂的数据,发现重复流产的原因. 然而,对于临床使用,必须克服数据限制等挑战.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 遗传学和免疫学 遗传学和免疫学
背景情况:
- 近一半的复发性流产 (RPL) 病例缺乏已知的原因,这表明当前诊断方法的局限性.
- 需要先进的方法来检测有助于RPL的微妙生物模式.
- 遗传学,免疫学和子宫内膜研究已取得进展,但尚未完全解释RPL病因.
研究的目的:
- 审查和评估人工智能 (AI) 对理解,预测和管理RPL的影响.
- 专注于机器学习 (ML) 来识别妊娠损失中的潜在生物途径.
- 探索AI在理解RPL多因素贡献者的作用.
主要方法:
- 对人工智能在生殖医学中的应用进行当前研究的叙述性回顾.
- 分析使用成像,蛋白质组,基因组,临床和多组数据进行RPL预测的研究.
- 包括使用人工智能开发RPL预测或机制模型的研究.
主要成果:
- 人工智能可以有效地检测RPL中的环境,免疫,生化和遗传因素之间的复杂相互作用.
- 机器学习和深度学习 (DL) 提高了预后准确性,并确定了RPL的新生物标志物.
- 人工智能通过整合多样化的数据,促进个性化的生殖形状,增强预测和咨询.
结论:
- 人工智能显示了个性化的RPL预测和改进机械理解的潜力.
- 人工智能在RPL中的临床翻译受到小型数据集,定义冲突和验证问题的阻碍.
- 解决数据多样性和前性试验等局限性对于将AI整合到RPL护理中至关重要.
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