Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction

The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recen...

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Published in:Reproductive biology and endocrinology 2024-05, Vol.22 (1), p.58-58, Article 58
Main Authors: Ma, Bing-Xin, Zhao, Guang-Nian, Yi, Zhi-Fei, Yang, Yong-Le, Jin, Lei, Huang, Bo
Format: Article
Language:English
Subjects:
Adult
Aneuploidy
Artificial intelligence
Blastocyst - cytology
Deep Learning
Embryo ploidy
Female
Fertilization in Vitro - methods
Genetic Testing - methods
Humans
iDAScore
Pregnancy
Preimplantation Diagnosis - methods
Retrospective Studies
Time-lapse system
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Summary:The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments. In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9. Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p 
ISSN:1477-7827
1477-7827
DOI:10.1186/s12958-024-01230-w