P002: Soluble CD146, an innovat ive and non-invasive biomarker of embryo selection for in-vitro fertilization

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Download Full-text

Detection of human embryo aneuploidy in the procedure of in vitro fertilization after preimplantation genetic screening using array comparative genomic hybridization

Medicinski pregled ◽

10.2298/mpns1710325v ◽

2017 ◽

Vol 70 (9-10) ◽

pp. 325-331

Author(s):

Jelena Vukosavljevic ◽

Aleksandra Trninic-Pjevic ◽

Artur Bjelica ◽

Ivana Jagodic ◽

Vesna Kopitovic ◽

...

Keyword(s):

In Vitro Fertilization ◽

Comparative Genomic Hybridization ◽

Genetic Screening ◽

Array Comparative Genomic Hybridization ◽

Preimplantation Genetic Screening ◽

Embryo Selection ◽

Comparative Genomic ◽

Genomic Hybridization ◽

Vitro Fertilization

Introduction. Numerical aberrations (whole chromosomal aneuploidy) have been considered one of the most important factors leading to implantation failure and early miscarriages in patients undergoing assisted reproductive procedures. Embryo selection is mainly based on morphological assessment; however, embryos produced from aneuploid gametes cannot be distinguished from euploid based on morphological characteristics. Detection of aneuploidy in human embryos. Thanks to the introduction of molecular-genetic screening of embryos, it is possible to identify aneuploid embryos via preimplantation genetic screening/diagnosis and thus select the best embryos based on their ploidy. Array comparative genomic hybridization is a molecular technique which allows ploidy analysis of the entire genome amplification from a single cell, within 24 hours after polar body, blastomere or trophectoderm cell biopsy. Trophectoderm cell biopsy is considered the most reliable screening approach given the lower mosaicism appearance at the blastocyst stage. Conclusion. This paper points to the importance and necessity of molecular analysis in embryo selection. Further investigations and improvements are required, because this technology has only recently become available in clinical practice in the in vitro fertilization procedure.

Download Full-text

Sperm Selection for In Vitro Fertilization

SciVee ◽

10.4016/9409.01 ◽

2008 ◽

Keyword(s):

In Vitro Fertilization ◽

Sperm Selection ◽

Vitro Fertilization ◽

Selection For

Download Full-text

Individualized Embryo Selection Strategy Developed by Stacking Machine Learning Model for Better In Vitro Fertilization Outcomes: An Application Study.

10.21203/rs.3.rs-140383/v1 ◽

2021 ◽

Author(s):

Qingsong XI ◽

Qiyu YANG ◽

Meng WANG ◽

Bo HUANG ◽

Bo ZHANG ◽

...

Keyword(s):

Machine Learning ◽

In Vitro Fertilization ◽

Endometrial Thickness ◽

Learning System ◽

Embryo Selection ◽

Selection Strategy ◽

Application Study ◽

Vitro Fertilization ◽

The Impact

Abstract Background: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods: This was an application study including 7887 patients with 8585 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.Results: For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1+P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1×P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion: Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

Download Full-text

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

10.21203/rs.3.rs-617438/v1 ◽

2021 ◽

Author(s):

Li Chen ◽

Wen Li ◽

Yuxiu Liu ◽

Zhihang Peng ◽

Liyi Cai ◽

...

Keyword(s):

Machine Learning ◽

In Vitro Fertilization ◽

Learning Algorithm ◽

Embryo Selection ◽

Human Embryos ◽

Utilization Rate ◽

Grading System ◽

Vitro Fertilization ◽

Embryo Grading

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

Download Full-text