351. Non-invasive embryo selection strategy for clinical IVF to avoid wastage of potentially competent embryos.
- Author
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Chen, Li, Li, Wen, Liu, Yuxiu, Peng, Zhihang, Cai, Liyi, Zhang, Ningyuan, Xu, Juanjuan, Wang, Liang, Teng, Xiaoming, Yao, Yaxin, Zou, Yangyun, Ma, Menglin, Liu, Jianqiao, Lu, Sijia, Sun, Haixiang, and Yao, Bing
- Subjects
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EMBRYOS , *FERTILIZATION in vitro , *EMBRYO transfer , *RANDOM forest algorithms , *BIRTH rate - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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