Nuevas metodologías para la selección no invasiva de embriones humanos en tratamientos de reproducción asistida: introducción de la inteligencia artificial en los laboratorios de fecundación in vitro

La evaluación y selección de embriones en los tratamientos de fecundación in vitro (FIV) se realiza manualmente de forma convencional, mediante observaciones puntuales bajo el microscopio durante el desarrollo embrionario. Dicho análisis altera las condiciones de cultivo, pudiendo afectar a las tasas de éxito del tratamiento de reproducción asistida. La introducción de la microscopía de lapso de tiempo o time-lapse, ha hecho posible un seguimiento continuo de los embriones in vitro. De esta forma, se obtiene rutinariamente gran cantidad de información en formato de imágenes grabadas. Hoy en día, el análisis de este material todavía se realiza manualmente y las imágenes se utilizan sobre todo cualitativamente. Otra fuente de información acerca de la viabilidad de los embriones in vitro es el medio de cultivo en el que se incuban durante su desarrollo. Numerosos estudios avalan la importancia de la interacción entre el embrión y el tracto reproductor femenino mediante ligandos y receptores durante la fase preimplantacional. Actualmente, el avance en las técnicas de análisis de proteómica permite conocer los valores de numerosos marcadores simultáneamente en el medio de cultivo embrionario. Nosotros hipotetizamos que se podría considerar el uso de herramientas y técnicas computacionales para aprovechar esta gran cantidad de datos y mejorar la evaluación de los embriones. Mediante el uso de la inteligencia artificial (IA), se podrían identificar particularidades conocidas y desconocidas que caractericen un embrión con alto potencial de implantación. Información adicional resultante de la incubación in vitro (tanto las imágenes time-lapse, como el perfil secretómico), podría medirse con esta tecnología, ya que es capaz de analizar cantidades masivas de datos. El objetivo general de esta tesis es definir nuevas metodologías no invasivas como herramienta de apoyo en los laboratorios de fecundación in vitro para seleccionar qué embrión transferir a la paciente, desarrollando y aplicando innovadoras tecnologías como la inteligencia artificial para automatizar y mejorar la evaluación in vitro. Para ello, se plantearon los siguientes objetivos específicos: a) describir parámetros del desarrollo embrionario y su relación con el potencial de implantación; b) identificar marcadores no invasivos del secretoma embrionario y su asociación con el éxito de un tratamiento de reproducción asistida; c) desarrollar modelos basados en inteligencia artificial para predecir resultados clínicos; y d) evaluar herramientas para la automatización de la selección embrionaria en los laboratorios de fecundación in vitro. La presente investigación de tesis doctoral se presenta como un compendio de tres publicaciones con datos relevantes para avanzar en la selección embrionaria con métodos no invasivos, señalando a la inteligencia artificial como herramienta de apoyo fundamental en los laboratorios de fecundación in vitro. Los siguientes artículos componen este compendio: I. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Bori L, Paya E, Alegre L, Viloria TA, Remohi JA, Naranjo V, Meseguer M. Fertil Steril. 2020 Dec;114(6):1232-1241. doi: 10.1016/j.fertnstert.2020.08.023. Epub 2020 Sep 8. PMID: 32917380. 5-year impact factor: 8,109. El objetivo principal de este estudio fue predecir el potencial de implantación del embrión utilizando nuevos parámetros no invasivos observados con sistemas time-lapse. Para ello, propusimos algunos parámetros morfodinámicos del embrión, que no habían sido evaluados hasta el momento, y analizamos su asociación con la probabilidad de implantación. Finalmente, desarrollamos un modelo utilizando redes neuronales artificiales para predecir el éxito de la implantación. II. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Bori L, Dominguez F, Fernandez EI, Del Gallego R, Alegre L, Hickman C, Quiñonero A, Nogueira MFG, Rocha JC, Meseguer M. Reprod Biomed Online. 2021 Feb;42(2):340-350. doi: 10.1016/j.rbmo.2020.09.031. Epub 2020 Oct 8. PMID: 33279421. 5-year impact factor: 4,603 El objetivo del presente estudio fue desarrollar un modelo de predicción de nacimientos vivos basado en la inteligencia artificial. Se utilizaron datos procedentes del análisis de imagen de blastocistos e información proteómica del medio de cultivo embrionario para predecir el potencial de un embrión euploide para dar lugar a un nacimiento vivo. III. The higher the score, the better the clinical outcome: retrospective evaluation of automatic embryo grading as a support tool for embryo selection in IVF laboratories. Bori L, Meseguer F, Valera MA, Galan A, Remohi J, Meseguer M. Hum Reprod. 2022 May 30;37(6):1148-1160. doi: 10.1093/humrep/deac066. PMID: 35435210. 5-year impact factor: 5,632 El objetivo principal de este estudio fue evaluar la utilidad de una puntuación embrionaria automática como herramienta de apoyo a la toma de decisiones en los laboratorios de FIV. En primer lugar, analizamos la asociación entre la puntuación del embrión y una serie de resultados clínicos, como la ploidía, el embarazo, la implantación y el nacimiento vivo. En segundo lugar, cuantificamos la contribución de la puntuación del embrión en los resultados de implantación y nacimiento vivo en diferentes escenarios en un contexto individualizado.Embryo evaluation and selection in in vitro fertilization (IVF) treatments is performed manually in a conventional way, through specific observations under the microscope during the embryo development in vitro. Such analysis alters the culture conditions and may affect the success rates of assisted reproduction treatment. The introduction of time-lapse microscopy has made it possible to continuously monitor embryos in vitro. In this way, a large amount of information is routinely obtained in the form of recorded images. Today, the analysis of this material is still performed manually, and the images are mostly used qualitatively. Another source of information about the viability of in vitro embryos is the culture medium in which they are incubated during their development. Numerous studies support the importance of the interaction between the embryo and the female reproductive tract by means of ligands and receptors during the preimplantation phase. Currently, advances in proteomic analysis techniques make it possible to know the values of numerous markers simultaneously in the embryo culture medium. We hypothesize that the use of computational tools and techniques could be considered to take advantage of this large amount of data and improve embryo evaluation. Using artificial intelligence (AI), known and unknown particularities that characterize an embryo with high implantation potential could be identified. Information resulting from in vitro incubation (both time-lapse images and secretomic profiling) could be measured with this technology, as it can analyze massive amounts of data. The general objective of this thesis is to define new non-invasive methodologies as a support tool in in vitro fertilization laboratories to select which embryo to transfer to the patient, developing and applying innovative technologies such as artificial intelligence to automate and improve in vitro evaluation. To this end, the following specific objectives were proposed: a) to describe parameters of embryo development and their relationship with implantation potential; b) to identify non-invasive markers of the embryonic secretome and their association with the success of assisted reproduction treatment; c) to develop models based on artificial intelligence to predict clinical outcomes; and d) to evaluate tools for the automation of embryo selection in in vitro fertilization laboratories. The present doctoral thesis is presented as a compendium of three publications with relevant data to advance embryo selection with non-invasive methods, highlighting artificial intelligence as a key support tool in in vitro fertilization laboratories. The following articles make up this compendium: I. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Bori L, Paya E, Alegre L, Viloria TA, Remohi JA, Naranjo V, Meseguer M. Fertil Steril. 2020 Dec;114(6):1232-1241. doi: 10.1016/j.fertnstert.2020.08.023. Epub 2020 Sep 8. PMID: 32917380. 5-year impact factor: 8,109. The main aim of this study was to predict embryo implantation potential using new non-invasive parameters observed with time-lapse systems. For this purpose, we proposed new morphodynamic parameters of the embryo, which had not been evaluated so far, and analyzed their association with the probability of implantation. Finally, we developed a model using artificial neural networks to predict implantation success. II. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Bori L, Dominguez F, Fernandez EI, Del Gallego R, Alegre L, Hickman C, Quiñonero A, Nogueira MFG, Rocha JC, Meseguer M. Reprod Biomed Online. 2021 Feb;42(2):340-350. doi: 10.1016/j.rbmo.2020.09.031. Epub 2020 Oct 8. PMID: 33279421. 5-year impact factor: 4,603 The aim of the present study was to develop an artificial intelligence-based live birth prediction model. Data from blastocyst image analysis and proteomic information from the embryo culture medium were used to predict the potential of a euploid embryo to lead to a live birth. III. The higher the score, the better the clinical outcome: retrospective evaluation of automatic embryo grading as a support tool for embryo selection in IVF laboratories. Bori L, Meseguer F, Valera MA, Galan A, Remohi J, Meseguer M. Hum Reprod. 2022 May 30;37(6):1148-1160. doi: 10.1093/humrep/deac066. PMID: 35435210. 5-year impact factor: 7,736 The main objective of this study was to evaluate the usefulness of an automated embryo score as a decision support tool in IVF laboratories. First, we analyzed the association between embryo scoring and several clinical outcomes, such as embryo ploidy, pregnancy, implantation, and live birth. Second, we quantified the contribution of embryo score on implantation and live birth outcomes in different scenarios in an individualized context

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

[ES] Objetivo: Describir nuevas características de embriones capaces de predecir el potencial de implantación como datos de entrada para un modelo de red neuronal artificial (ANN). Diseño: Estudio de cohorte retrospectivo. Entorno: Centro de FIV privado afiliado a la universidad. Paciente (s): Este estudio incluyó a 637 pacientes del programa de donación de ovocitos que se sometieron a transferencia de un solo blastocisto durante dos años consecutivos. Intervención (es): Ninguna. Principales medidas de resultado: La investigación se dividió en dos fases. La fase 1 consistió en la descripción y análisis de las siguientes características embrionarias en embriones implantados y no implantados: distancia y velocidad de migración pronuclear, diámetro del blastocisto expandido, área de masa celular interna y duración del ciclo celular del trofoectodermo. La fase 2 consistió en el desarrollo de un algoritmo ANN para la predicción de la implantación. Se obtuvieron resultados para cuatro modelos alimentados con diferentes datos de entrada. El poder predictivo se midió con el uso del área bajo la curva característica operativa del receptor (AUC). Resultado (s): De los cinco nuevos parámetros descritos, el diámetro expandido del blastocisto y la duración del ciclo celular del trofoectodermo tenían valores estadísticamente diferentes en los embriones implantados y no implantados. Después de que los modelos ANN fueron entrenados y validados mediante validación cruzada cinco veces, estos fueron capaces de predecir la implantación en los datos de prueba con AUC de 0,64 para ANN1 (morfocinética convencional), 0,73 para ANN2 (morfodinámica novedosa), 0,77 para ANN3 (morfocinética convencional þ morfodinámica novedosa) y 0,68 para ANN4 (variables discriminatorias de prueba estadística). Conclusión (es): Las nuevas características embrionarias propuestas afectan al potencial de implantación y su combinación con parámetros morfocinéticos convencionales es eficaz como datos de entrada para un modelo predictivo basado en inteligencia artificial.[EN] 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 thorn 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. ((c) 2020 by American Society for Reproductive Medicine.)Supported by the Ministry of Science, Innovation, and Universities CDTI (IDI-20191102), an Industrial Ph.D. grant (DIN2018-009911), and Agencia Valenciana de Innovacio (INNCAD00-18-009) to E.P. and M.M.Bori, L.; Paya-Bosch, E.; Alegre, L.; Viloria, T.; Remohí, J.; Naranjo Ornedo, V.; Meseguer, M. (2020). Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertility and Sterility. 114(6):1232-1241. https://doi.org/10.1016/j.fertnstert.2020.08.023S123212411146De Geyter, C., Calhaz-Jorge, C., Kupka, M. S., Wyns, C., Mocanu, E., Motrenko, T., … Goossens, V. (2018). 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Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques

[EN] Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter-and intra-observer variability. Automation of this process results in more objective and accurate predictions.Method: In this paper, we propose a novel methodology based on deep learning to automatically evaluate the morphological appearance of human embryos from time-lapse imaging. A supervised contrastive learning framework is implemented to predict embryo viability at day 4 and day 5, and an inductive transfer approach is applied to classify embryo quality at both times.Results: Results showed that both methods outperformed conventional approaches and improved state-ofthe-art embryology results for an independent test set. The viability result achieved an accuracy of 0.8103 and 0.9330 and the quality results reached values of 0.7500 and 0.8001 for day 4 and day 5, respectively. Furthermore, qualitative results kept consistency with the clinical interpretation. Conclusions: The proposed methods are up to date with the artificial intelligence literature and have been proven to be promising. Furthermore, our findings represent a breakthrough in the field of embryology in that they study the possibilities of embryo selection at day 4. Moreover, the grad-CAMs findings are directly in line with embryologists' decisions. Finally, our results demonstrated excellent potential for the inclusion of the models in clinical practice.This work has been partially funded by Agencia Valenciana de la Innovacion (AVI) (2002-VLC-011-MM) . The work of Elena Pay Bosch has been supported by the Spanish Government (DIN2018-009911) and the work of Valery Naranjo Ornedo by the Generalitat Valenciana (AEST/2021/054) . We gratefully acknowledge the support from the Generalitat Valenciana with the donation of the DGX A100 used for this work, action co-nanced by the European Union through the Operational Program of the European Regional Development Fund of the Co-munitat Valenciana 2014-2020 (IDIFEDER/2020/030) .Paya-Bosch, E.; Bori, L.; Colomer, A.; Meseguer, M.; Naranjo Ornedo, V. (2022). Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques. Computer Methods and Programs in Biomedicine. 221(106895):1-12. https://doi.org/10.1016/j.cmpb.2022.10689511222110689

Sperm kinematic, head morphometric and kinetic-morphometric subpopulations in the blue fox (Alopex lagopus)

This work provides information on the blue fox ejaculated sperm quality needed for seminal dose calculations. Twenty semen samples, obtained by masturbation, were analyzed for kinematic and morphometric parameters by using CASA-Mot and CASA-Morph system and principal component (PC) analysis. For motility, eight kinematic parameters were evaluated, which were reduced to PC1, related to linear variables, and PC2, related to oscillatory movement. The whole population was divided into three independent subpopulations: SP1, fast cells with linear movement; SP2, slow cells and nonoscillatory motility; and SP3, medium speed cells and oscillatory movement. In almost all cases, the subpopulation distribution by animal was significantly different. Head morphology analysis generated four size and four shape parameters, which were reduced to PC1, related to size, and PC2, related to shape of the cells. Three morphometric subpopulations existed: SP1: large oval cells; SP2: medium size elongated cells; and SP3: small and short cells. The subpopulation distribution differed between animals. Combining the kinematic and morphometric datasets produced PC1, related to morphometric parameters, and PC2, related to kinematics, which generated four sperm subpopulations - SP1: high oscillatory motility, large and short heads; SP2: medium velocity with small and short heads; SP3: slow motion small and elongated cells; and SP4: high linear speed and large elongated cells. Subpopulation distribution was different in all animals. The establishment of sperm subpopulations from kinematic, morphometric, and combined variables not only improves the well-defined fox semen characteristics and offers a good conceptual basis for fertility and sperm preservation techniques in this species, but also opens the door to use this approach in other species, included humans