Non-invasive embryo selection strategy for clinical IVF to avoid wastage of potentially competent embryos

• DOI: 10.1016/j.rbmo.2022.03.006
• Publication Date: 2022-03-11T16:47:44Z
• Authors: Li Chen; Wen Li; Yuxiu Liu; Zhihang Peng; Liyi Cai; Ningyuan Zhang; Juanjuan Xu; Liang Wang; Xiaoming Teng; Yaxin Yao; Yangyun Zou; Menglin Ma; Jianqiao Liu; Sijia Lu; Haixiang Sun; Bing Yao
• Keywords: Embryo Culture Techniques; 03 medical and health sciences; Blastocyst; 0302 clinical medicine; Pregnancy; Humans; Female; Fertilization in Vitro; Genetic Testing; Aneuploidy; Preimplantation Diagnosis

Can a non-invasive embryo transfer strategy provide a reference for embryo selection to be established?Chromosome 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).Higher 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.A machine learning guided embryo grading system can be used to optimize embryo selection and avoid wastage of potential embryos.