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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Guanqiao Shan, Khaled Abdalla, Hang Liu, Changsheng Dai, Justin Tan, Junhui Law, Carolyn Steinberg, Ang Li, Iryna Kuznyetsova, Zhuoran Zhang, Clifford Librach, Yu Sun
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	BMC 2024-10-01
Sarja:	Reproductive Biology and Endocrinology
Aiheet:	Blastocyst Euploidy prediction 3D morphology measurement Machine learning
Linkit:	https://doi.org/10.1186/s12958-024-01302-x
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