Improving embryo ploidy prediction: a machine learning approach using morphokinetic meta-variables and clinical data

2025/11/09 by Enric Güell-Penas, E Güell Penas, Minerva Ferrer-Buitrago +16 · 1 voice
Medicine · #Ovarian function and disorders #Prenatal Screening and Diagnostics #Reproductive Biology and Fertility

paper · doi:10.1016/j.rbmo.2025.105404

openalex created_date 2025/11/09 · openalex publication_date 2025/11/09 · crossref created 2025/11/09 · crossref issued 2026/07/01 · crossref published 2026/07/01 · crossref published-print 2026/07/01 · crossref deposited 2026/07/11 · crossref indexed 2026/07/11 · openalex updated_date 2026/07/22

Citations

• Increasing the probability of selecting chromosomally normal embryos by time-lapse morphokinetics analysis
• Time-lapse cinematography of dynamic changes occurring during in vitro development of human embryos
• Calculating the sample size required for developing a clinical prediction model

Discussions

• This study effectively utilized a comprehensive array of morphokinetic data to develop a multicentre machine learning model capable of differentiating between aneuploid embryos and those with the pote [bsky, 1 points, 0 comments]