人工智能服务于子宫内授精和自发周期的定时性交
Michal Youngster1, Almog Luz2, Micha Baum3
1IVF Unit, Department of Obstetrics and Gynecology, Shamir Medical Center, Zerifin, Israel; Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel.
Fertility and sterility
|July 25, 2023
概括
一个机器学习模型准确地预测排卵时间和受精或定时性交 (TI) 的肥沃窗口. 这种人工智能工具仅使用血液测试,通过提高预测准确性和效率来提高受孕机会.
科学领域:
- 生殖内分泌学和不孕不育问题
- 医学中的人工智能
- 生物统计学和机器学习
背景情况:
- 准确预测排卵对于最大限度地提高自然生育治疗中的受孕率至关重要.
- 目前用于排卵预测的方法通常依赖于素激素 (LH) 激增,这可能是可变的.
- 需要更精确,更有效的方法来确定最佳受精窗口.
研究的目的:
- 开发和验证用于预测排卵时间的机器学习模型.
- 为了确定子宫内授精或定时性交 (TI) 的最佳窗口.
- 仅使用血液测试数据来评估模型的准确性.
主要方法:
- 对2467个自然周期-冷胚胎移植周期的回顾性队列研究.
- 开发一个NGBoost机器学习模型和一个治疗管理算法.
- 模型训练了雌激素,孕激素和黄化激素水平.
- 使用专家意见和超声波确定的排卵数据进行验证.
主要成果:
- 机器学习模型在预测排卵和最佳授精日方面取得了很高的准确性.
- 在"专家"组中,92.9%的病例有正确的排卵预测和授精时间.
- 该模型展示了效率,平均每周期2.78-2.85次测试.
结论:
- 这项研究介绍了第一个仅使用血液检测的机器学习模型,用于准确的授精计划.
- 该模型能够整合多个因素,超越LH激增,改善排卵预测的模型的能力.
- 该模型的实施可以提高生育治疗的准确性和效率,从而可能增加受孕率.
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