Morphokinetics associated with artificial intelligence for the prediction of human blastocyst

• Grant number: 18/24252-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: February 01, 2019
• End date: February 28, 2021
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: José Celso Rocha
• Grantee: Eleonora Inácio Fernandez
• Host Institution: Faculdade de Ciências e Letras (FCL-ASSIS). Universidade Estadual Paulista (UNESP). Campus de Assis. Assis , SP, Brazil

Abstract

In view of the growing demand for in vitro fertilization methods, the need for a constant search for improvement in its techniques has been observed. One of the most important steps in assisted reproduction is the way embryos are classified and selected since this selection will have a direct impact on the success or failure of the procedure. Generally, the choice of the embryos to be implanted is performed by embryologists through microscopy, normally following the classification system of Gardner & Schoolcraft (1999), and this analysis is performed when the embryo is in a blastocyst stage. The fact that development occurs until the stage of the blastocyst is an important milestone for the development and demonstration of embryonic aptitude. Embryos that do not reach the blastocyst stage are unable to maintain a gestation. Through the introduction of mathematical techniques of artificial neural networks and genetic algorithms, this study aims to present an alternative for the improvement of the embryonic selection, in which the prediction of reaching the blastocyst stage would be carried out by means of an in silico analysis of the cleavage times presented by the embryo in the first hours after fertilization.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FERNANDEZ, ELEONORA INACIO; FERREIRA, ANDRE SATOSHI; CECILIO, MATHEUS HENRIQUE MIQUELAO; CHELES, DORIS SPINOSA; DE SOUZA, REBECA COLAUTO MILANEZI; NOGUEIRA, MARCELO FABIO GOUVEIA; ROCHA, JOSE CELSO. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. JOURNAL OF ASSISTED REPRODUCTION AND GENETICS, v. 37, n. 10, JUL 2020. (18/24252-2, 18/19371-2, 17/19323-5)

CHELES, DORIS SPINOSA; FERREIRA, ANDRE SATOSHI; DE JESUS, ISABELA SUEITT; FERNANDEZ, ELEONORA INACIO; PINHEIRO, GABRIEL MARTINS; DAL MOLIN, ELOIZA ADRIANE; ALVES, WALLACE; MILANEZI DE SOUZA, REBECA COLAUTO; BORI, LORENA; MESEGUER, MARCOS; et al. An Image Processing Protocol to Extract Variables Predictive of Human Embryo Fitness for Assisted Reproduction. APPLIED SCIENCES-BASEL, v. 12, n. 7, p. 20-pg., 2022-04-01. (20/07634-9, 18/24252-2, 19/26749-4, 17/19323-5, 19/26684-0)

BORI, LORENA; DOMINGUEZ, FRANCISCO; FERNANDEZ, ELEONORA INACIO; DEL GALLEGO, RAQUEL; ALEGRE, LUCIA; HICKMAN, CRISTINA; QUINONERO, ALICIA; NOGUEIRA, MARCELO FABIO GOUVEIA; ROCHA, JOSE CELSO; MESEGUER, MARCOS. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Reproductive BioMedicine Online, v. 42, n. 2, p. 340-350, FEB 2021. (18/24252-2, 18/19371-2)