Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study
Abstract Background Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete feat...
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
BMC
2024-10-01
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| Collection: | Reproductive Biology and Endocrinology |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1186/s12958-024-01302-x |
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