基于深度学习的个性化FSH实时剂量的预测在GnRH激动剂长期协议中
Na Kong1,2,3, Yu Xia4, Zhilong Wang2,3
1Nanjing Drum Tower Hospital, Drum Tower Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210008, Jiangsu, China.
Journal of translational medicine
|May 15, 2025
概括
一个新的深度学习模型预测每天的卵泡刺激激素 (FSH) 剂量,以实现个性化的受控卵巢刺激 (COS). 这种人工智能方法改进了传统方法,在辅助生殖中提供了更好的结果.
科学领域:
- 生殖内分泌学 生殖内分泌学
- 人工智能在医学中的应用
- 辅助生殖技术 辅助生殖技术
背景情况:
- 个性化卵泡刺激激素 (FSH) 剂量对于优化辅助生殖控制卵巢刺激 (COS) 的结果至关重要.
- 目前用于确定FSH剂量的方法通常是静态的,不允许实时调整,阻碍了个性化治疗.
- 患者异质性在COS期间实现最佳和一致的结果方面是一个重大挑战.
研究的目的:
- 开发和评估一种新的深度学习模型,用于在整个COS过程中预测个性化的每日FSH剂量.
- 为了实现基于个体患者反应的FSH剂量的实时,动态调整.
- 提高辅助生殖中COS协议的精度和有效性.
主要方法:
- 开发了一个使用跨时间和跨特征编码 (CTFE) 的深度学习模型来预测每日FSH剂量.
- 对13788次试管婴儿/ICSI周期 (2018-2020年) 的回顾性分析,包括7到30之间毛囊数量的患者.
- 该模型将静态 (年龄,BMI,荷尔蒙水平) 和动态 (毛囊发育,荷尔蒙趋势) 数据整合到刺激日,使用K-最近邻近算法预测最终剂量.
主要成果:
- 在CTFE深度学习模型的测试组中,CTFE深度学习模型实现了0.737的剂量分类准确度和0.732的加权F1得分.
- 该模型在第1天和第5天的刺激中显著优于传统的LASSO回归模型 (例如,第1天的F1得分为0.832和0.699).
- 准确的预测性能在第13天之后仍然保持,即使在更长的刺激周期中数据可用性降低,使用滑动窗口机制.
结论:
- 这项研究引入了第一个跨时间和跨特征的深度学习框架,用于COS中的每日个性化FSH剂量预测.
- 与传统方法相比,CTFE模型表现出优越的性能,为标准化COS管理提供了一个有前途的工具.
- 建议进行进一步的前性多中心验证,以确认该模型的临床实用性和可通用性,以改善COS结果.
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