Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential.

Journal: Fertility and sterility

Published Date: Dec 1, 2020

OBJECTIVE: To describe novel embryo features capable of predicting implantation potential as input data for an artificial neural network (ANN) model.

Lorena Bori

IVI-RMA Valencia, Valencia, Spain. Electronic address: lorena.bori@ivirma.com.
Elena Paya

IVI-RMA Valencia, Valencia, Spain; Instituto de Investigación e Innovación en Bioingeniería, Universitat Politécnica de Valencia, Valencia, Spain.
Lucia Alegre

IVI-RMA Valencia, Valencia, Spain.
Thamara Alexandra Viloria

IVI-RMA Valencia, Valencia, Spain.
Jose Alejandro Remohi

IVI-RMA Valencia, Valencia, Spain.
Valery Naranjo

Instituto de Investigación e Innovación en Bioingeniería, I3B, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
Marcos Meseguer

Instituto Valenciano de Infertilidad (IVI) Valencia, INCLIVA-Universidad de Valencia, Valencia, Spain.

Blastocyst Embryo Implantation Female Fertility Fertilization in Vitro Humans Image Interpretation, Computer-Assisted Infertility Neural Networks, Computer Oocyte Donation Predictive Value of Tests Pregnancy Pregnancy Rate Retrospective Studies Single Embryo Transfer Time Factors Time-Lapse Imaging Treatment Outcome

View on PubMed Access via DOI PubMed (32917380)