AIMC Topic: Blastocyst
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Development and validation of machine learning models for predicting blastocyst yield in IVF cycles.
Scientific reports
Jul 2, 2025
Predicting blastocyst formation poses significant challenges in reproductive medicine and critically influences clinical decision-making regarding extended embryo culture. While previous research has primarily focused on determining whether an IVF cy...
An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.
Human reproduction (Oxford, England)
Jun 1, 2025
STUDY QUESTION: Can a quantitative method be developed to differentiate between blastocysts with similar or same inner cell mass (ICM) and trophectoderm (TE) grades, while also reflecting their potential for live birth?
Deep learning classification integrating embryo images with associated clinical information from ART cycles.
Scientific reports
May 21, 2025
An advanced Artificial Intelligence (AI) model that leverages cutting-edge computer vision techniques to analyse embryo images and clinical data, enabling accurate prediction of clinical pregnancy outcomes in single embryo transfer procedures. Three ...
Artificial intelligence-driven analysis of embryo morphokinetics in singleton, twin, and triplet pregnancies.
Human reproduction (Oxford, England)
Mar 1, 2025
In recent years, the transfer of more than one embryo has become less frequent to diminish multiple pregnancies. Even so, there is still a risk of one embryo splitting into two or even three. This report presents the case of a triamniotic monochorion...
The 'golden fleece of embryology' eludes us once again: a recent RCT using artificial intelligence reveals again that blastocyst morphology remains the standard to beat.
Human reproduction (Oxford, England)
Jan 1, 2025
Grading of blastocyst morphology is used routinely for embryo selection with good outcomes. A lot of effort has been placed in IVF to search for the prize of selecting the most viable embryo to transfer ('the golden fleece of embryology'). To improve...
Time will tell: time-lapse technology and artificial intelligence to set time cut-offs indicating embryo incompetence.
Human reproduction (Oxford, England)
Dec 1, 2024
STUDY QUESTION: Can more reliable time cut-offs of embryo developmental incompetence be generated by combining time-lapse technology (TLT), artificial intelligence, and preimplantation genetics screening for aneuploidy (PGT-A)?
[Application of the blastomere count variations "skip value" in the embryo AI assessment].
Zhonghua fu chan ke za zhi
Jul 25, 2024
To explore the correlation between blastomere count variations "skip value" which extracted from by time-lapse technology (TLT) combined with artificial intelligence (AI) and morphological features of in vitro fertilization (IVF) embryo, and to test...
Embryonic Quality Assessment using Advanced Deep Learning Architectures utilizing Microscopic Images of Blastocysts.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The accurate evaluation of embryonic quality which is a key aspect in Assisted Reproductive Technology (ART) and it is crucial to ensure the success of in vitro fertilization (IVF), especially at critical developmental phases like day 3 and day 5. To...
Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.
Human reproduction (Oxford, England)
Jun 3, 2024
STUDY QUESTION: Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts?
BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.
Human reproduction (Oxford, England)
Apr 3, 2024
STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?
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