A Lightweight Transformer Approach for Predicting Blastocyst Formation on Limited Embryo Images

In vitro fertilisation (IVF) is a widely adopted assisted reproductive technology that facilitates embryo development in cases of infertility. A critical step in this process is identifying which embryos successfully develop into blastocysts. The process needs continuous video monitoring, which is time-consuming and expensive. In this paper, we show a new deep learning method that predicts blastocyst formation by using just one image per day from each embryo, not full videos. Our method uses DINOv2 to extract detailed features from embryo images and a lightweight Video Vision Transformer (ViViT) version to analyze and classify the image sequences. Our experiments show that the proposed model can predict blastocyst and non-blastocyst formation with 95.7% accuracy. The proposed approach minimizes the reliance on costly continuous video monitoring, indicating that more affordable equipment could be used without compromising precision. This improves both the practicality and cost-efficiency of implementation in IVF clinics, supporting embryologists in making more confident embryo selection decisions.