Classification of human embryos using the techniques of time-lapse, digital image processing and Artificial Intelligence.

• Grant number: 17/19323-5
• Support Opportunities: Regular Research Grants
• Start date: August 01, 2018
• End date: July 31, 2020
• Field of knowledge: Health Sciences - Medicine
• Principal Investigator: José Celso Rocha
• Grantee: José Celso Rocha
• Host Institution: Faculdade de Ciências e Letras (FCL-ASSIS). Universidade Estadual Paulista (UNESP). Campus de Assis. Assis , SP, Brazil
• Associated researchers: Marcelo Fábio Gouveia Nogueira

Abstract

Assisted reproduction techniques, although they have evolved a lot in recent years, continue to present a low birth rate per treatment cycle. This has led clinics specializing in human reproduction to seek more effective methodologies that complement and maximize in vitro fertilization treatment. In this sense, a quality of human embryos in the blastocyst stage has been shown to be able to predict the embryo's success rate in establishing a clinical pregnancy. Currently, although there are several techniques for analyzing the morphological quality of the embryo, many of them are invasive and incompatible for a healthy gestation, which leads the clinics to routinely analyze the embryo through microscopy. However, this analysis is very subjective, since there may be sharp divergences in intra- and inter-embryologist classification. Artificial intelligence techniques associated with digital image processing and the time-lapse method may fill this demand. These techniques are noninvasive, they first involve an image from a human embryo taken by the time-lapse technique, and this image is then digitally processed for extraction of mathematical variables. Then these are analyzed by the technique of artificial intelligence, which can withdraw the subjectivity involved in the classic method of embryo classification described above. Thus, the evaluation becomes objective and reproducible and, in this way, could provide the patients involved in the process with better information about their real chances of becoming pregnant and a better prediction of the minimum number of embryos to be transferred to obtain single fetus gestation. (AU)

Scientific publications

(6)

(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)

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)

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; DAL MOLIN, ELOIZA ADRIANE; ROCHA, JOSE CELSO; GOUVEIA NOGUEIRA, MARCELO FABIO. Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service. JORNAL BRASILEIRO DE REPRODUCAO ASSISTIDA, v. 24, n. 4, p. 470-479, OCT-DEC 2020. (12/50533-2, 17/19323-5)

DE LOS ANGELES VALERA, MARIA; CELSO ROCHA, JOSE; BORI, LORENA; ALEGRE, LUCIA; GOUVEIA NOGUEIRA, MARCELO FABIO; SPINOSA CHELES, DORIS; MESEGUER, MARCOS. NOVEL ARTIFICIAL INTELLIGENCE ALGORITHM FOR IMPROVING EMBRYO SELECTION COMBINING MORPHOKINETICS AND NON-INVASIVE MEASUREMENT OF OXIDATIVE STRESS.. Fertility and Sterility, v. 114, n. 3, p. 2-pg., 2020-09-01. (18/19053-0, 17/19323-5)

GOUVEIA NOGUEIRA, MARCELO FIBIO; GUILHERME, VITRIA BERTOGNA; PRONUNCIATE, MICHELI; DOS SANTOS, PRISCILA HELENA; BEZERRA DA SILVA, DIOGO LIMA; ROCHA, JOS CELSO. Artificial Intelligence-Based Grading Quality of Bovine Blastocyst Digital Images: Direct Capture with Juxtaposed Lenses of Smartphone Camera and Stereomicroscope Ocular Lens. SENSORS, v. 18, n. 12, DEC 2018. (17/19323-5, 12/50533-2)

ALEGRE, LUCIA; BORI, LORENA; DE LOS ANGELES VALERA, MARIA; GOUVEIA NOGUEIRA, MARCELO FABIO; SATOSHI FERREIRA, ANDRE; CELSO ROCHA, JOSE; MESEGUER, MARCOS. FIRST APPLICATION OF ARTIFICIAL NEURONAL NETWORKS FOR HUMAN LIVE BIRTH PREDICTION ON GERI TIME-LAPSE MONITORING SYSTEM BLASTOCYST IMAGES.. Fertility and Sterility, v. 114, n. 3, p. 1-pg., 2020-09-01. (17/19323-5)