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

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Authors:

Lorena Bori, M.Sc., Elena Paya, M.Sc., Lucia Alegre, M.Sc., Thamara Alexandra Viloria, Ph.D., Jose Alejandro Remohi, M.D., Ph.D., Valery Naranjo, Ph.D., Marcos Meseguer, Ph.D.

Abstract:

Objective

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


Design

Retrospective cohort study.


Setting

University-affiliated private IVF center.


Patient(s)

This study included 637 patients from the oocyte donation program who underwent single-blastocyst transfer during two consecutive years.


Intervention(s)

None.


Main Outcome Measure(s)

The research was divided into two phases. Phase 1 consisted of the description and analysis of the following embryo features in implanted and nonimplanted embryos: distance and speed of pronuclear migration, blastocyst expanded diameter, inner cell mass area, and trophectoderm cell cycle length. Phase 2 consisted of the development of an ANN algorithm for implantation prediction. Results were obtained for four models fed with different input data. The predictive power was measured with the use of the area under the receiver operating characteristic curve (AUC).


Result(s)

Out of the five novel described parameters, blastocyst expanded diameter and trophectoderm cell cycle length had statistically different values in implanted and nonimplanted embryos. After the ANN models were trained and validated using fivefold cross-validation, they were capable of predicting implantation on testing data with AUCs of 0.64 for ANN1 (conventional morphokinetics), 0.73 for ANN2 (novel morphodynamics), 0.77 for ANN3 (conventional morphokinetics + novel morphodynamics), and 0.68 for ANN4 (discriminatory variables from statistical test).


Conclusion(s)

The novel proposed embryo features affect the implantation potential, and their combination with conventional morphokinetic parameters is effective as input data for a predictive model based on artificial intelligence.

Fertility and Sterility

Editorial Office, American Society for Reproductive Medicine

Fertility and Sterility® is an international journal for obstetricians, gynecologists, reproductive endocrinologists, urologists, basic scientists and others who treat and investigate problems of infertility and human reproductive disorders. The journal publishes juried original scientific articles in clinical and laboratory research relevant to reproductive endocrinology, urology, andrology, physiology, immunology, genetics, contraception, and menopause. Fertility and Sterility® encourages and supports meaningful basic and clinical research, and facilitates and promotes excellence in professional education, in the field of reproductive medicine.

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