Artificial intelligence in assisted reproductive techniques: comparative evaluation of deep learning architectures for bovine cumulus-oocyte complexes classification
Makaleler > Tam Makale · 2025
Yazarlar: Aylin GÖKHAN, Seda Çetinkaya Karabekir, Emre Ölmez, Saadet Özen Akarca Dizakar, Mert Can Tekel, Orhan Er, Mehmet Kemal Güllü, Türker Çavuşoğlu
Ege Tıp Dergisi, Uluslararası, 2025, cilt 64, ss 653-664
DOI: 10.19161/etd.1737365
Özet
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.