AI Medical Compendium Journal:
Human reproduction (Oxford, England)

Showing 1 to 10 of 28 articles

An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades.

Human reproduction (Oxford, England)
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?

Artificial intelligence-driven analysis of embryo morphokinetics in singleton, twin, and triplet pregnancies.

Human reproduction (Oxford, England)
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...

Artificial intelligence-based tissue segmentation and cell identification in multiplex-stained histological endometriosis sections.

Human reproduction (Oxford, England)
STUDY QUESTION: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?

Ethics of artificial intelligence in embryo assessment: mapping the terrain.

Human reproduction (Oxford, England)
Artificial intelligence (AI) has the potential to standardize and automate important aspects of fertility treatment, improving clinical outcomes. One promising application of AI in the fertility clinic is the use of machine learning (ML) tools to ass...

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)
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)
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)?

Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?

Human reproduction (Oxford, England)
Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing par...

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.

Human reproduction (Oxford, England)
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)
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?

ChatGPT: a reliable fertility decision-making tool?

Human reproduction (Oxford, England)
The internet is the primary source of infertility-related information for most people who are experiencing fertility issues. Although no longer shrouded in stigma, the privacy of interacting only with a computer provides a sense of safety when engagi...