使用深度学习模型的反复植入失败的免疫风险因素:一个多中心的回顾性队列研究
Mohsen Dashti1,2, Afsaneh Ghasemzadeh1,2, Sare Doustfateme3,4
1Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Scientific reports
|December 1, 2025
概括
一种深度学习模型准确地预测了复发性植入失败 (RIF) 患者的活产. 关键因素包括年龄,BMI和免疫标记,有助于针对不孕症的个性化治疗策略.
科学领域:
- 生殖免疫学 生殖免疫学
- 人工智能在医学中的应用
背景情况:
- 复发性植入失败 (RIF) 仍然是辅助生殖技术 (ART) 的一个重大障碍.
- 了解导致RIF的免疫因素对于改善妊娠结果至关重要.
研究的目的:
- 开发和验证一个深度学习模型 (TabNet),用于预测RIF患者的活产.
- 确定RIF中活产的关键预测变量,包括免疫学标记.
主要方法:
- 对2463名没有妇科异常的RIF患者进行了回顾性分析.
- 使用23个变量开发TabNet深度学习模型.
- 用ROC曲线和校准图表对患者特征的统计比较和模型性能评估.
主要成果:
- 塔布网模型在预测活产婴儿时获得了87.4%的准确率和0.952的AUROC.
- 该模型发现的重要预测特征包括年龄,Th1/Th2比率,BMI,抗甲状腺过氧化酶 (anti-TPO),抗核抗体 (ANA),抗dsDNA和抗组织转胺酶 (anti-TTG).
结论:
- 在RIF患者中,TabNet模型表现出强大的活产预测性能.
- 这种人工智能驱动的方法可以提高对RIF机制的理解,并指导免疫疗法患者分层.
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