Machine Learning-Guided Noninvasive Embryo Selection for Clinical in Vitro Fertilization Treatment to Avoid Wasting Potentially Qualified Embryos

Abstract BackgroundThe success rates of in vitro fertilization (IVF) treatment are limited by the aneuploidy of human embryos. Pre-implantation genetic testing for aneuploidy(PGT-A) is often used to select embryos with normal ploidy but requires invasive embryo biopsy. MethodsWe performed chromosome sequencing of 345 paired blastocyst culture medium and whole blastocyst samples and developed a noninvasive embryo grading system 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 performed to investigate clinical outcomes between machine learning-guided and traditional niPGT-A analyses. We graded embryos as A, B, or C using machine learning-guided niPGT-A analysis according to their euploidy probability levels predicted by noninvasive chromosomal screening. ResultsWe observed higher live birth rate 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 machine learning strategy compared to the conventional dichotomic judgment of euploidy or aneuploidy in the niPGT-A analysis (78.8% versus 57.9%, p<0.001). We observed better outcomes in A- and B-grade embryos versus C-grade embryos and higher embryo utilization rates through machine learning strategies than traditional niPGT-A analysis. ConclusionThese results demonstrate that the machine learning-guided embryo grading system can optimize embryo selection and avoid wasting potential embryos.Trial registrationChinese Clinical Trial Registry,ChiCTR-RRC-17010396.Registered 11 January 2017, http://www.chictr.org.cn/ChiCTR-RRC-17010396