Li Chen1, Wen Li2, Yuxiu Liu3, Zhihang Peng4, Liyi Cai5, Ningyuan Zhang6, Juanjuan Xu1, Liang Wang2, Xiaoming Teng7, Yaxin Yao8, Yangyun Zou8, Menglin Ma8, Jianqiao Liu9, Sijia Lu10, Haixiang Sun11, Bing Yao12. 1. Department of Reproductive Medicine, Affiliated Jinling Hospital, Medicine School of Nanjing University, Nanjing 210002, People's Republic of China. 2. Reproductive Medical Center, Changzheng Hospital, Second Military Medical University Shanghai 200003, People's Republic of China. 3. Department of Medical Statistics, Jinling Hospital, Southern Medical University, Nanjing Jiangsu 210002, People's Republic of China. 4. Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing Jiangning District 211166, People's Republic of China. 5. Reproductive Medical Center of Hebei Maternity Hospital, Shijiazhuang Hebei 050000, People's Republic of China. 6. Reproductive Medicine Center, Nanjing Drum Tower Hospital, Nanjing University School of Medicine, Nanjing 210008, People's Republic of China. 7. Shanghai First Maternity and Infant Hospital School of Medicine, Tongji University Shanghai 200040, People's Republic of China. 8. Department of Clinical Research, Yikon Genomics Company, Ltd., Suzhou 215000, People's Republic of China. 9. Center for Reproductive Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, People's Republic of China. 10. Department of Clinical Research, Yikon Genomics Company, Ltd., Suzhou 215000, People's Republic of China. Electronic address: lusijia@yikongenomics.com. 11. Reproductive Medicine Center, Nanjing Drum Tower Hospital, Nanjing University School of Medicine, Nanjing 210008, People's Republic of China. Electronic address: stevensunz@163.com. 12. Department of Reproductive Medicine, Affiliated Jinling Hospital, Medicine School of Nanjing University, Nanjing 210002, People's Republic of China. Electronic address: yaobing@nju.edu.cn.
Abstract
RESEARCH QUESTION: Can a non-invasive embryo transfer strategy provide a reference for embryo selection to be established? DESIGN: 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). RESULTS: 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. CONCLUSION: A machine learning guided embryo grading system can be used to optimize embryo selection and avoid wastage of potential embryos.
RESEARCH QUESTION: Can a non-invasive embryo transfer strategy provide a reference for embryo selection to be established? DESIGN: 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). RESULTS: 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. CONCLUSION: A machine learning guided embryo grading system can be used to optimize embryo selection and avoid wastage of potential embryos.