This project aims to develop an AI-assisted decision-support system for embryo quality assessment in in-vitro fertilization (IVF) laboratories. Current evaluation methods, such as the Gardner grading system, rely on subjective visual assessment, leading to high inter- and intra-observer variability that affects clinical decisions and training consistency. The proposed system will combine computer vision and natural […]
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