胚胎质量
胚胎
原核
活产
体外受精
生物
非整倍体
卵裂球
人工智能
怀孕
合子
计算机科学
遗传学
胚胎发生
染色体
基因
作者
Ling Sun,Jiahui Li,Simiao Zeng,Qiangxiang Luo,Hanpei Miao,Yunhao Liang,Linling Cheng,Zhuo Sun,Wa Hou Tai,Yibing Han,Yun Yin,Keliang Wu,Kang Zhang
标识
DOI:10.1097/cm9.0000000000003162
摘要
In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable.
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