Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model.

ABSTRACT: Within the medical field of human assisted reproductive technology, a method for interpretable, non-invasive, and objective oocyte evaluation is lacking. To address this clinical gap, a workflow utilizing machine learning techniques has been developed involving automatic multi-class segmentation of two-dimensional images, morphometric analysis, and prediction of developmental outcomes of mature denuded oocytes based on feature extraction and clinical variables. Two separate models have been developed for this purpose-a model to perform multiclass segmentation, and a classifier model to classify oocytes as likely or unlikely to develop into a blastocyst (Day 5-7 embryo). The segmentation model is highly accurate at segmenting the oocyte, ensuring high-quality segmented images (masks) are ut

SUBMITTER: Fjeldstad J 

PROVIDER: S-EPMC11078996 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature