Open AccessA Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics
Author(s) -
Gunawan B. Danardono,
Alva Erwin,
James Purnama,
Nining Handayani,
Arie A Polim,
Arief Boediono,
Ivan Sini
Publication year - 2022
Publication title -
journal of reproduction and infertility
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 22
eISSN - 2251-676X
pISSN - 2228-5482
DOI - 10.18502/jri.v23i4.10809
Subject(s) - artificial intelligence , convolutional neural network , computer science , hyperparameter , transfer of learning , artificial neural network , annotation , machine learning , deep learning , homogeneous , pattern recognition (psychology) , thermodynamics , physics
The purpose of the current study was to reduce the risk of human bias in assessing embryos by automatically annotating embryonic development based on their morphological changes at specified time-points with convolutional neural network (CNN) and artificial intelligence (AI).