EmbryoVision AI: An explainable deep learning framework for enhanced blastocyst selection in assisted reproductive technologies

Abstract

Accurate embryo selection is a key factor in improving implantation success rates in Assisted Reproductive Technologies. This study presents a deep learning framework, EmbryoVision AI, designed to enhance blastocyst assessment using Time-Lapse Imaging and eXplainable AI techniques. A customized convolutional neural network was developed to capture both morphological and temporal dynamics, enabling a precise classification of the embryo. To ensure transparency, Gradient-weighted Class Activation Mapping was integrated, allowing visualization of decision-critical embryonic structures and ensuring clinical alignment. The model demonstrated strong predictive performance across different embryo grades, achieving an accuracy of 91.5% for Grade AA, 88.4% for Grade AB, and 79.3% for Grade BC. The AUC-ROC values were 0.95, 0.90, and 0.81 for Grade AA, AB, and BC, respectively, indicating strong discriminatory capabilities. The findings suggest that AI-driven embryo selection can enhance objectivity, reduce human variability, and improve ART outcomes. However, the results also underscore the need to refine AI models to better handle morphological variability in lower-quality embryos, highlighting the importance of improving generalization and strengthening clinical integration.