Acute prediction of SNPs (Single Nucleotide Polymorphisms) from high throughput sequencing data is a challenging problem, having potential to explore possible variation within plants species. For the extraction of profitable information from bulk of ...
RESEARCH QUESTION: Can a deep machine learning artificial intelligence algorithm predict ploidy and implantation in a known data set of static blastocyst images, and how does its performance compare against chance and experienced embryologists?
Reproductive biology and endocrinology : RB&E
34903224
BACKGROUND: For the association between time-lapse technology (TLT) and embryo ploidy status, there has not yet been fully understood. TLT has the characteristics of large amount of data and non-invasiveness. If we want to accurately predict embryo p...
RESEARCH QUESTION: Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data i...
BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer var...
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) ...
OBJECTIVE: To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10-115 hours after insemination (hpi).
This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic testing in in vitro fertilization procedures. The current gold standard, preimplantation genetic te...
Journal of assisted reproduction and genetics
38963605
PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.