@article{article_1737365, title={Artificial intelligence in assisted reproductive techniques: comparative evaluation of deep learning architectures for bovine cumulus-oocyte complexes classification}, journal={Ege Tıp Dergisi}, volume={64}, pages={653–664}, year={2025}, DOI={10.19161/etd.1737365}, url={https://izlik.org/JA42NS66UJ}, author={Gökhan, Aylin and Çetinkaya Karabekir, Seda and Ölmez, Emre and Akarca Dizakar, Saadet Özen and Tekel, Mert Can and Er, Orhan and Güllü, Mehmet Kemal and Çavuşoğlu, Türker}, keywords={Artificial Intelligence, Convolutional Neural Networks, Xception41, Deep Learning, Swin Transformer, Cumulus-Oocyte Complexes, Oocyte Classification, Assisted Reproductive Technologies}, abstract={Aim: Follicular quality is a key determinant of success in assisted reproductive technologies, directly affecting outcomes such as fertilization, embryo development, implantation, and live birth rates. However, conventional assessment of cumulus-oocyte complexes relies on subjective morphological evaluation, introducing variability and reducing consistency in clinical decision-making. Materials and Methods: A comparative evaluation of various pre-trained deep learning architectures—including both convolutional neural networks and transformer-based models—was conducted for the automated morphological grading of bovine cumulus-oocyte complexes into four quality categories (Grade A–D). A dataset of 1,400 annotated images of cumulus-oocyte complexes, enhanced through data augmentation techniques to increase image diversity, was used for model training and validation. Results: Among the tested architectures, Xception41 (a variant of convolutional neural networks) and Swin Transformer (a transformer-based model) achieved the highest performance, with test accuracies of 74.75% and 73.25%, and macro F1-scores of 0.75 and 0.74, respectively. While both models performed well in grading cumulus-oocyte complexes with distinct morphological features (Grades 3 and 4), classification accuracy decreased for the more subtle differences between Grades 1 and 2. Furthermore, most models exhibited signs of overfitting under the current training configuration. Conclusion: This study demonstrates the potential of deep learning-based approaches to standardize and enhance the efficiency of cumulus-oocyte complexes evaluation in assisted reproductive technologies. Further optimization is needed to improve model generalization and to address challenges in grading morphologically similar follicular structures.}, number={4}, organization={Bu çalışma, İzmir Bakırçay Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından BBAP.2022.006 Proje Numarası ile desteklenmiştir.}