Non-invasive embryo selection strategy for clinical IVF to avoid wastage of potentially competent embryos.
Reprod Biomed Online
; 45(1): 26-34, 2022 07.
Article
em En
| MEDLINE
| ID: mdl-35537927
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%, Pâ¯=â¯0.006) and B- versus C-grade embryos (45.3% versus 27.1%, Pâ¯=â¯0.022) and lower miscarriage rate in A- versus C-grade embryos (15.9% versus 33.3%, Pâ¯=â¯0.026) and B- versus C-grade embryos (14.3% versus 33.3%, Pâ¯=â¯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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Diagnóstico Pré-Implantação
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
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Prognostic_studies
Limite:
Female
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Humans
/
Pregnancy
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article