Use of artificial intelligence in the prediction of human births based on embryonic morphokinetics

• Grant number: 18/19371-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: February 01, 2019
• End date: January 31, 2020
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: José Celso Rocha
• Grantee: Matheus Henrique Miquelão Cecilio
• Host Institution: Faculdade de Ciências e Letras (FCL-ASSIS). Universidade Estadual Paulista (UNESP). Campus de Assis. Assis , SP, Brazil

Abstract

The search for assisted reproduction techniques, mainly in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) has increased in last years. Although the current success rates of reproduction clinics, many people seeking treatment still face problems to generate a pregnancy. The experience of passing for several attempts to become pregnant can take men and women to adverse consequences as feeling rage, sadness, loneliness, could arrive to the anxiety development and depression. The credibility given to FIV and ICSI in the last years increased with the progress of time-lapse techniques. This non-invasive methodology obtain the characteristics of the embryonic development. Searching for tools that characterize and aid in the selection of the best embryos, as time-lapse techniques do, the use of artificial intelligence such as artificial neural networks (ANNs) and genetic algorithms (GAs) may be a good alternative, allowing an increase of the current rates of implantation and gestation. Therefore, this research project intends, through the use of ANNs and GAs, provide, through the concepts and patterns of human embryonic morphokinetics, a better prediction of embryos more suitable for transference, allowing an increase of the current rates of implantation and gestation, providing an increase of livebirths.

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)

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)