Innovative AI models for clinical decision-making: predicting blastocyst formation and quality from time-lapse embryo images up to embryonic day 3

Abstract

Accurate embryo assessment on embryonic day 3 of assisted reproductive technology (ART) is crucial for deciding whether to continue the culture until day 5 (blastocyst stage) or opt for earlier transfer or cryopreservation. Prolonged culture often improves pregnancy outcomes in patients with multiple high-quality embryos, but may offer limited benefits for older patients or those with few available embryos. In Japan, where donor eggs are rarely used, cleavage-stage vitrification remains common in poor-prognosis cases, making early embryo assessment clinically relevant. To address this clinical challenge, analyzing embryo quality in early stages by artificial intelligence (AI) can be useful. We retrospectively analyzed images of 7111 two-pronuclear embryos (Veeck grade ≤3) using four different time-lapse incubators. We fine-tuned ImageNet-1k-pretrained NASNet-A Large to automatically classify each time-lapse image into 17 morphological categories, including cell stages and Veeck grades 1-3. This model achieved 95 % cell-stage accuracy on the test set. We combined these annotations with age at egg retrieval in a gradient boosting framework (XGBoost) to predict blastocyst formation, good blastocysts, and poor blastocyst + arrested embryos (PBAE). The ROC AUCs were 0.87, 0.88, and 0.87 for blastocyst formation, good blastocysts, and PBAE, respectively, indicating good predictive performance for day 3 embryo assessment. Notably, the PBAE model reached a precision-recall AUC of 0.90, accurately identifying embryos unlikely to benefit from extended culture. This novel AI prediction model could ensure transparency and addresses the "black box" limitation often associated with AI. By integrating a high-accuracy auto-annotation pipeline with interpretable AI (via SHapley Additive exPlanations), our device-independent approach supports appropriate embryo-specific decisions, potentially reducing unnecessary culture, optimizing workflows, and improving clinical outcomes in ART.

Keywords: Artificial intelligence; Assisted reproductive technology; Blastocyst; Machine learning; Prediction model; Reproductive medicine; Time-lapse.