与反复流产相关的免疫生活方式模式的基于人工智能的多变量分析:一项探索性回顾性研究
Mohsen Dashti1,2, Lida Aslanian-Kalkhoran1,2, Sare Doustfateme3,4
1Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Scientific reports
|March 5, 2026
概括
一个深度学习模型在复发性流产 (RPL) 患者中确定了免疫生活方式模式. 这种方法为了解RPL机制和开发有针对性的治疗提供了高可靠性.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 免疫学 免疫学 免疫学
背景情况:
- 复发性流产 (RPL) 影响1-5%的怀孕.
- 早期发现高风险因素对于有效治疗和减少RPL发病率至关重要.
研究的目的:
- 开发一种深度学习模型,用于识别RPL患者的免疫生活方式模式.
- 利用临床和实验室数据进行早期RPL风险评估.
主要方法:
- 从五家伊朗诊所收集了16818名RPL患者和19979名对照者的回顾性数据.
- 应用TabNet深度学习模型,使用22个临床和实验室特征.
- 严格的模型评估使用AUC,准确性,精度,特异性,灵敏性和交叉验证.
主要成果:
- 深度学习模型表现出强大的性能,AUC为0.985.
- 实现了高精度 (0.946),精度 (0.936),特异性 (0.921) 和灵敏度 (0.968) 的高精度.
- 通过5倍的交叉验证,防止过度装配,确保模型可靠性.
结论:
- 深度学习有效地确定了与反复流产相关的可靠的免疫生活方式模式.
- 这些发现有助于人们更好地了解RPL的生物学基础.
- 这项研究为制定有针对性的RPL治疗策略提供了基础.
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