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深度学习模型:它们与胚胎幼和生殖结果的关系

Aikaterini Selntigia¹, Lucia Maresca¹, Diletta Montanino Oliva¹

¹IVIRMA Global Research Alliance, IVIRMA Roma, 00161 Rome, Italy.

|August 28, 2025

概括

智能数据分析 (iDA) 评分,是一种深度学习模型,可预测胚胎胚积并改善试管婴儿的结果. 虽然它不是PGT-A的替代品,但在无法进行遗传检测的情况下,它可以协助胚胎选择.

科学领域:

生殖医学
医疗保健中的人工智能
胚胎学

背景情况:

在体外受精 (IVF) 中的胚胎选择旨在确定具有最高生殖潜力的胚胎.
植入前遗传检测 (PGT-A) 是标准的,但具有侵入性,并不总是可行的.
深度学习 (DL) 模型,如iDA评分,提供非侵入性的胚胎评估替代方案.

研究的目的:

批判性地评估iDAScore (1.0和2.0版本),胚胎繁体化和临床结果 (活产,流产) 之间的关系.
与染色体状态和生殖结果相比,评估iDAScore的预测性能.

主要方法:

从2020年1月到2025年5月发表的研究的叙述性回顾.
在PubMed和Google Scholar上使用关键词进行搜索:"iDAScore"",深度学习"",euploidy"",活产".
包括英语全文研究,评估iDAScore对染色体状况或生殖结果的预测准确性.

主要成果:

包括六项回顾性研究,所有这些研究都显示了较高的iDAScore值与胚胎繁殖之间的显著关联.
曲线下的面积 (AUC) 值为euploidy预测范围从0. 60到0. 68.
iDAScore与活产率呈正相关性,与流产率呈负相关性,对欧体群的准确性中等.

结论:

iDAScore显示出在试管婴儿中作为辅助工具的潜力.
在PGT-A不可行的情况下,它可能有助于优先转移胚胎.
需要进一步的大规模前性研究来证实其临床效用.

关键词:

深度学习化症在 iDAScore 生活出生率

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