可解释的人工智能识别毛囊,在辅助受孕期间优化临床结果
Simon Hanassab1,2,3, Scott M Nelson4,5, Artur Akbarov2
1Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
Nature communications
|January 8, 2025
概括
优化体外受精 (IVF) 涉及专注于中间毛囊大小,以获得成熟的卵细胞. 这种方法可以通过避免与负面结果相关的较大的毛囊来提高活产率.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 不孕症影响了很大一部分夫妇,通常需要体外受精 (IVF).
- 目前的试管婴儿决策往往依赖于简化方法,没有充分利用复杂的患者数据.
- 机器学习为数据驱动的洞察提供了潜在的潜力,以提高试管婴儿的结果.
研究的目的:
- 应用可解释的人工智能 (AI) 来识别影响试管婴儿结果的关键卵泡大小特征.
- 确定最佳的卵泡尺寸分布,以最大限度地提高成熟卵细胞产量和活产率.
- 为了研究毛囊大小,孕激素水平和新鲜胚胎移植中的活产率之间的关系.
主要方法:
- 这是一项多中心研究,涉及11个欧洲试管婴儿中心的19082名未经治疗的女性患者.
- 利用可解释的AI来分析与临床结果相关的毛囊大小数据.
- 与卵细胞成熟,孕激素升高和活产率相关的卵泡大小指标.
主要成果:
- 中等大小的卵泡对于检索到的成熟卵细胞数量最为关键.
- 在刺激结束时,更高比例的中间毛囊与改善的活产率相关.
- 较大的平均毛囊大小 (>18毫米) 与孕激素升高和新鲜移植的活产率降低有关.
结论:
- 可解释的人工智能可以识别特定的卵泡大小模式,这对于优化试管婴儿至关重要.
- 根据卵泡大小分布来个性化卵巢刺激可能会提高活产率.
- 人工智能驱动的洞察力有望改善试管婴儿个性化和成功率,这证明了进一步的前性研究.
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