一种基于人工智能的方法,用于选择在对手协议周期中触发最佳日期
Shachar Reuvenny1, Michal Youngster2, Almog Luz1
1FertilAI, Ramat-Gan, Israel.
Reproductive biomedicine online
|November 20, 2023
概括
一个机器学习模型可以确定最佳的触发日,以最大限度地提高在对手周期中的卵细胞检索率. 这种人工智能方法改善了结所有和新鲜转移周期的结果.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 卵巢刺激协议 卵巢刺激协议
背景情况:
- 反对者协议周期在辅助生殖技术中被广泛使用.
- 优化触发日选择对于最大限度地提高卵细胞产量至关重要.
- 目前的触发日选择依赖于医生的经验,这可能是可变的.
研究的目的:
- 开发和评估一种机器学习模型,用于在对手周期中建议最佳触发日.
- 为了最大限度地提取总和成熟 (代相II) 卵细胞的数量.
- 为了比较模型建议的触发日与医生选择的日.
主要方法:
- 对9622个抗剂周期 (2018-2022) 的回顾性队列研究.
- 一个XGBoost机器学习算法被训练来预测最佳触发日.
- 模型的性能被评估在"结所有卵细胞"",化所有"和"仅ICSI"的测试集.
主要成果:
- 该模型根据预测结果建议1,2,或3个触发日选项.
- 一致的触发日 (模型与医生) 在所有测试组中都显示了卵细胞和胚胎的显著增加 (P < 0.001).
- 平均增加包括4.8个总卵细胞和3.4个MII卵细胞在"结所有"周期.
结论:
- 机器学习可以优化在对手周期中的触发日选择.
- 这种人工智能驱动的方法可能会增强卵细胞检索并改善周期结果.
- 实施可以导致更标准化,针对患者的特定协议和更好的医生决策.
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