Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation.

Journal: Reproductive biomedicine online

PMID: 37866216

Abstract

RESEARCH QUESTION: Do cell numbers and degree of fragmentation in cleavage-stage embryos, assessed manually, correlate with evaluations made by deep learning algorithm model iDAScore v2.0?

Authors

Aisling Ahlström

IVIRMA Global Research Alliance, LIVIO, Göteborg, Sweden.
Jørgen Berntsen

Vitrolife A/S, Denmark.
Martin Johansen

Vitrolife A/S, Aarhus, Denmark.
Christina Bergh

Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden.
Danilo Cimadomo

GeneraLife IVF, Clinica Valle Giulia, Rome, Italy. Electronic address: cimadomo@generaroma.it.
Thorir Hardarson

Vitrolife Sweden AB, Gothenburg, Sweden.
Kersti Lundin

Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden.

Keywords

Cell Count Deep Learning Embryo Transfer Embryo, Mammalian Female Fertilization in Vitro Humans Live Birth Pregnancy Pregnancy, Multiple Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (37866216)

Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation.

Abstract

Authors

Keywords

External Resources

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