An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study

Author(s)

Bori, Lorena ^[1] ; Dominguez, Francisco ^{[2, 3]} ; Fernandez, Eleonora Inacio ^[4] ; Del Gallego, Raquel ^[3] ; Alegre, Lucia ^[1] ; Hickman, Cristina ^[5] ; Quinonero, Alicia ^[3] ; Nogueira, Marcelo Fabio Gouveia ^[4] ; Rocha, Jose Celso ^[4] ; Meseguer, Marcos ^{[1, 2]}

Affiliation

^[1] IVI Valencia, IVF Lab, Valencia - Spain

^[2] Hlth Res Inst Fe, Valencia - Spain

^[3] Inst Univ IVI IUIVI, IVI Fdn, Valencia - Spain

^[4] Univ Estadual Paulista Unesp, Fac Ciencias & Letras, Campus Assis, Sao Paulo, SP - Brazil

^[5] Imperial Coll, Inst Reprod & Dev Biol, Hammersmith Campus, London - England

• Document type: Journal article
• Source: Reproductive BioMedicine Online; v. 42, n. 2, p. 340-350, FEB 2021.
• Web of Science Citations: 0

Abstract

Research question: The study aimed to develop an artificial intelligence model based on artificial neural networks (ANNs) to predict the likelihood of achieving a live birth using the proteomic profile of spent culture media and blastocyst morphology. Design: This retrospective cohort study included 212 patients who underwent single blastocyst transfer at IVI Valencia. A single image of each of 186 embryos was studied, and the protein profile was analysed in 81 samples of spent embryo culture medium from patients included in the preimplantation genetic testing programme. The information extracted from the analyses was used as input data for the ANN. The multilayer perceptron and the back-propagation learning method were used to train the ANN. Finally, predictive power was measured using the area under the curve (AUC) of the receiver operating characteristic curve. Results: Three ANN architectures classified most of the embryos correctly as leading (LB+) or not leading (LB-) to a live birth: 100.0% for ANN1 (morphological variables and two proteins), 85.7% for ANN2 (morphological variables and seven proteins), and 83.3% for ANN3 (morphological variables and 25 proteins). The artificial intelligence model using information extracted from blastocyst image analysis and concentrations of interleukin-6 and matrix metalloproteinase-1 was able to predict live birth with an AUC of 1.0. Conclusions: The model proposed in this preliminary report may provide a promising tool to select the embryo most likely to lead to a live birth in a euploid cohort. The accuracy of prediction demonstrated by this software may improve the efficacy of an assisted reproduction treatment by reducing the number of transfers per patient. Prospective studies are, however, needed. (AU)

• FAPESP's process: 18/24252-2 - Morphokinetics associated with artificial intelligence for the prediction of human blastocyst
• Grantee: Eleonora Inácio Fernandez
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 18/19371-2 - Use of artificial intelligence in the prediction of human births based on embryonic morphokinetics
• Grantee: Matheus Henrique Miquelão Cecilio
• Support Opportunities: Scholarships in Brazil - Scientific Initiation