[en] The advent of assisted reproductive technologies marks a revolutionary stride in the annals of biological science, signaling a new era not only in human healthcare but also in wildlife conservation. Infertility, affecting approximately 15\% of couples globally, poses a significant challenge to the dream of parenthood. Assisted reproduction emerges as a ray of hope, offering innovative solutions such as In Vitro Fertilization and surrogacy, which have already brought joy to millions of families worldwide. In 2018 alone, IVF procedures resulted in the birth of over 2.5 million babies, reshaping the landscape of modern family dynamics. Expanding the horizon beyond human concerns, the narrative then delves into the world of mammals, particularly endangered species. In this realm, assisted reproduction transforms from medical interventions to instruments of conservation. With over 1,000 mammalian species currently facing the threat of extinction, the application of techniques like artificial insemination and embryo transfer becomes pivotal, underscoring their role in maintaining the delicate balance of our ecosystems. At the heart of these advancements lies the flourishing field of artificial intelligence. Artificial intelligence's integration into assisted reproduction is not just innovative; it's revolutionary. Algorithms are enhancing the precision and efficiency of assisted reproductive technologies, from analyzing genetic data to improving success rates of treatments, to predicting the viability of embryos. This convergence of artificial intelligence and assisted reproduction is a prime example of how interdisciplinary approaches are pushing the boundaries of what's possible, offering hope where there was once despair. This thesis, firstly, embarks on an insightful journey into the role of cumulus oocyte complexes in ensuring healthy oocyte and embryo development in mammals. It inspects closely and thoroughly several aspects such as the expansion during the immature and mature phases, and the cell density. To dissect these biological phenomena, advanced deep learning techniques and neural networks have been harnessed. We developed pipelines capable of evaluating the most effective scoring methods, subsequently enabling the automatic segmentation and estimation of crucial study elements. The findings underscore a compelling link between expansion and density, and the development of a healthy embryo. This revelation holds profound implications for the future trajectory of assisted reproduction, potentially revolutionizing our approach toward enhancing fertility treatments. Simultaneously, this thesis confronts the challenge of acquiring accurate labels for these complex biological structures. Label acquisition typically involves multiple experts, each bringing their own subjective perspectives to the determination of "true" segmentation labels. This diversity of opinion, while enriching, often introduces a level of noise that can set back the effectiveness of segmentation algorithms. To navigate this issue, we implemented coupled convolutional neural networks, tailored for a small-sized, real-world dataset, necessary for healthy embryo development. Our methodology pioneers a novel approach that focuses on areas of high uncertainty. By comprehensively analyzing these zones, we reach deeper insights into the individual characteristics of the structures in question. Having this, we were able to propose a more sophisticated ground truth, one that is refined through the use of maximum likelihood estimation. This approach contributes to the discussion of leveraging machine learning algorithms for segmenting medical images, especially in situations where multiple human annotators are involved. Through this dual exploration of cumulus oocyte complexes role and the refinement of best methods to disentangle from biases in experts' annotations, this thesis lays the groundwork for significant advancements in the realm of assisted reproduction. By introducing cutting-edge artificial intelligence methodologies to intricate biological processes, we uncover new avenues for research and application that promise to elevate the efficiency and performance of reproductive technologies.

[es] La introducción de tecnologías reproductivas asistidas marca un avance revolucionario en ciencia biológica, abriendo una nueva era en la salud humana y la conservación de vida silvestre. Aproximadamente el 15% de parejas enfrentan infertilidad globalmente, desafiando el sueño de ser padres. La reproducción asistida ofrece esperanza con soluciones como la Fertilización In Vitro (FIV) y la subrogación, alegrando a millones de familias. En 2018, la FIV generó más de 2.5 millones de nacimientos, transformando la dinámica familiar. Además, en la conservación de especies en peligro, estas técnicas pasan a ser herramientas vitales. Más de 1,000 especies de mamíferos están en riesgo de extinción, haciendo crucial la inseminación artificial y transferencia de embriones para mantener el equilibrio ecológico. La inteligencia artificial (IA) juega un papel central en la evolución de la reproducción asistida, mejorando la precisión y eficiencia desde el análisis genético hasta la predicción de viabilidad de embriones. Este cruce entre IA y reproducción asistida ejemplifica cómo enfoques interdisciplinarios superan límites, creando esperanza. La tesis explora el papel de complejos de oocitos cumulus en el desarrollo saludable de oocitos y embriones, utilizando técnicas avanzadas de aprendizaje profundo para evaluar y segmentar elementos de estudio esenciales, destacando la importancia de la expansión y densidad celular para un desarrollo embrionario saludable. Además, aborda el desafío de etiquetar estructuras biológicas complejas con múltiples expertos, introduciendo redes neuronales convolucionales acopladas para refinar la segmentación, lo que mejora la precisión en la identificación de características críticas para el desarrollo de embriones saludables. Este estudio avanza significativamente en reproducción asistida, aplicando IA en procesos biológicos para mejorar la eficiencia de las tecnologías reproductivas, abriendo nuevas direcciones en investigación y aplicación.