New frontiers in embryo selection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Genetics (clinical),Developmental Biology,Obstetrics and Gynecology,Genetics,Reproductive Medicine,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10815-022-02708-5.pdf
Reference49 articles.
1. Ahlstrom A, et al. Trophectoderm morphology: an important parameter for predicting live birth after single blastocyst transfer. Hum Reprod. 2011;26(12):3289–96.
2. Gardner DK, et al. Blastocyst score affects implantation and pregnancy outcome: towards a single blastocyst transfer. Fertil Steril. 2000;73(6):1155–8.
3. Bormann CL, et al. Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril. 2020;113(4):781-787 e1.
4. Curchoe C. Development of a mobile competency assessment platform for IVF laboratory quality management systems. J Assist Reprod Genet. 2018;35:2087–106.
5. Morbeck D, Hammond E, Kit AMF, Curchoe CL Assuring quality in embryology decision making: blastocyst grading agreement assessed via a smartphone application Hum Reprod 2021;36. https://doi.org/10.1093/humrep/deab130.166
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1. Improved pregnancy prediction performance in an updated deep-learning embryo selection model: a retrospective independent validation study;Reproductive BioMedicine Online;2024-01
2. Development of a Predictive Model for Optimization of Embryo Transfer Timing Using Blood-Based microRNA Expression Profile;International Journal of Molecular Sciences;2023-12-20
3. Correlations between a deep learning-based algorithm for embryo evaluation with cleavage-stage cell numbers and fragmentation;Reproductive BioMedicine Online;2023-12
4. Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study;Reproductive BioMedicine Online;2023-12
5. Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM);2023-07-30
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