胚胎
胚泡
非整倍体
男科
流产
胚胎移植
生物
选择(遗传算法)
妇科
胚胎发生
染色体
遗传学
医学
怀孕
机器学习
计算机科学
基因
作者
Li Chen,Wen Li,Yuxiu Liu,Zhihang Peng,Cai Lv,Ningyuan Zhang,Jian Xu,Liang Wang,Xiaoming Teng,Yingshui Yao,Yangyun Zou,Menglin Ma,Jianqiao Liu,Sijia Lü,Haixiang Sun,Bing Yao
标识
DOI:10.1016/j.rbmo.2022.03.006
摘要
Research questionCan a non-invasive embryo transfer strategy provide a reference for embryo selection to be established?DesignChromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples was carried out and a non-invasive embryo grading system was developed based on the random forest machine learning algorithm to predict blastocyst ploidy. The system was validated in 266 patients, and a blinded prospective observational study was conducted to investigate clinical outcomes between machine learning-guided and traditional non-invasive preimplantation genetic testing for aneuploidy (niPGT-A) analyses. Embryos were graded as A, B or C according to their euploidy probability levels predicted by non-invasive chromosomal screening (NICS).ResultsHigher live birth rate was observed in A- versus C-grade embryos (50.4% versus 27.1%, P = 0.006) and B- versus C-grade embryos (45.3% versus 27.1%, P = 0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, P = 0.026) and B- versus C-grade embryos (14.3% versus 33.3%, P = 0.021). The embryo utilization rate was significantly higher through the machine learning strategy than the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, P < 0.001). Better outcomes were observed in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through the machine learning strategy compared with traditional niPGT-A analysis.ConclusionA machine learning guided embryo grading system can be used to optimize embryo selection and avoid wastage of potential embryos.
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