This study aims to develop and validate a non-image, multimodal digital twin model of the human blastocyst using fully de-identified clinical, laboratory, molecular, biochemical, and long-term follow-up data obtained during routine IVF treatment. The dataset includes parental clinical background, IVF cycle parameters, embryo morphology in text format, PGT-A results, secretome and exosomal biomarkers, endometrial receptivity profiles, pregnancy course, delivery outcomes, and child development data up to 3 years of age. The purpose of this observational study is to create a longitudinal reference dataset linking embryo-level molecular and biochemical characteristics with clinical outcomes from implantation to early childhood. The digital twin model is intended to investigate predictors of implantation success, embryo viability, and early developmental trajectories without the use of images or videos. No investigational drugs or devices are used, and no procedures beyond standard clinical practice are added.
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Accuracy of Digital Twin Model in Predicting Embryo Implantation
Timeframe: From embryo transfer (Day 0) to confirmation of clinical pregnancy (up to 12 weeks of gestation).