[ca] La introducció de tecnologies reproductives assistides marca un avenç revolucionari en ciència biològica, obrint una nova era en la salut humana i la conservació de vida silvestre. Aproximadament el 15% de parelles enfronten infertilitat globalment, desafiant el somni de ser pares. La reproducció assistida ofereix esperança amb solucions com la Fecundació In Vitro (FIV) i la subrogació, alegrant a milions de famílies. En 2018, la FIV va generar més de 2,5 milions de naixements, transformant la dinàmica familiar. A més, en la conservació d'espècies en perill, aquestes tècniques es converteixen en eines vitals. Més de 1.000 espècies de mamífers estan en risc d'extinció, fent crucial la inseminació artificial i transferència d'embrions per mantenir l'equilibri ecològic. La intel·ligència artificial (IA) juga un paper central en l'evolució de la reproducció assistida, millorant la precisió i eficiència des de l'anàlisi genètic fins a la predicció de viabilitat d'embrions. Aquest encreuament entre IA i reproducció assistida exemplifica com enfocaments interdisciplinaris superen límits, creant esperança. La tesi explora el paper de complexos d'oòcits cumulus en el desenvolupament saludable d'oòcits i embrions, utilitzant tècniques avançades d'aprenentatge profund per avaluar i segmentar elements d'estudi essencials, destacant la importància de l'expansió i densitat cel·lular per a un desenvolupament embrionari saludable. A més, aborda el desafiament d'etiquetar estructures biològiques complexes amb múltiples experts, introduint xarxes neuronals convolucionals acoblades per refinar la segmentació, la qual cosa millora la precisió en la identificació de característiques crítiques per al desenvolupament d'embrions saludables. Aquest estudi avança significativament en reproducció assistida, aplicant IA en processos biològics per millorar l'eficiència de les tecnologies reproductives, obrint noves direccions en recerca i aplicació.

[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.