Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging.

ABSTRACT: Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver o

SUBMITTER: Rajendran S 

PROVIDER: S-EPMC11377764 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature