A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

ABSTRACT: The use of time lapse systems (TLS) in In Vitro Fertilization (IVF) labs to record developing embryos has paved the way for deep-learning based computer vision algorithms to assist embryologists in their morphokinetic evaluation. Today, most of the literature has characterized algorithms that predict pregnancy, ploidy or blastocyst quality, leaving to the side the task of identifying key morphokinetic events. Using a dataset of N = 1909 embryos collected from multiple clinics equipped with EMBRYOSCOPE/EMBRYOSCOPE+ (Vitrolife), GERI (Genea Biomedx) or MIRI (Esco Medical), this study proposes a novel deep-learning architecture to automatically detect 11 kinetic events (from 1-cell to blastocyst). First, a Transformer based video backbone was trained with a custom metric inspired by reverse c

SUBMITTER: Canat G 

PROVIDER: S-EPMC11584792 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature