EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool.
Commun Biol
; 7(1): 268, 2024 Mar 05.
Article
em En
| MEDLINE
| ID: mdl-38443460
ABSTRACT
The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report EVATOM a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fertilização in vitro
/
Embrião de Mamíferos
Limite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
Commun Biol
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos