A Comprehensive Survey on Deep Learning Techniques for Embryo Quality Assessment in IVF

In-vitro fertilization (IVF) has emerged as a vital assisted reproductive technology, offering hope to couples experiencing infertility. A critical determinant of IVF success is the accurate assessment of embryo quality, traditionally performed through manual visual grading, which is subjective and prone to inter-observer variability. Recent advancements in artificial intelligence, particularly deep learning, have shown remarkable potential in automating and enhancing embryo selection processes. This survey presents a comprehensive overview of state-of-the-art deep learning methodologies applied to embryo image analysis, quality prediction, and implantation success forecasting. It explores various architectures including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transformer-based models tailored for medical imaging. Public and private embryo datasets, as well as evaluation metrics such as accuracy, FID, and AUC, are discussed. Furthermore, the paper highlights key challenges such as data imbalance, interpretability, and generalization across clinics. Future research directions emphasize the integration of multi-modal data, real-time prediction systems, and ethical considerations in AI-driven IVF systems. This survey aims to guide researchers and clinicians in understanding the evolving landscape of embryo analysis through deep learning