Longitudinal Clinical Observation of a Digital Twin Model for Blastocyst Evaluation in IVF Clinics

Implantation, Embryo

IVF, Digital Twin, Embryo selection, Multi-omics, Pregnancy outcomes, AI modeling

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.

This observational study collects and integrates multimodal, non-image data from routine IVF cycles to construct digital twin models of human blastocysts. The dataset includes synchronized molecular, cellular, biochemical, and clinical parameters describing both the embryo and the maternal environment during implantation and early pregnancy. All information is fully de-identified and obtained as part of standard clinical care. Parental and Clinical Background The dataset incorporates: demographic factors, reproductive history, and relevant risk factors; karyotype results, thrombophilia and autoimmune screening; sperm DNA fragmentation indices; ovarian stimulation parameters and hormonal dynamics throughout the IVF cycle. IVF Laboratory Data Non-image embryologic information includes: oocyte maturity and fertilization method (e.g., ICSI); early cleavage development documented in descriptive text format (no images or videos); blastocyst grading; preimplantation genetic testing for aneuploidy (PGT-A), including ploidy status and mosaicism. Molecular and Secretome Data Embryo- and culture-media-associated biomarkers include: cytokines, growth factors, LIF, and metabolic indicators in spent media; exosomal microRNA signatures linked to implantation potential; transcriptomic and methylation profiles of trophectoderm samples when available. Endometrial and Immune Environment Maternal environment assessment includes: transcriptomic profiling of the endometrial receptivity window (ERA-like signatures); uterine immune parameters such as uNK cell activity and T-regulatory balance. Pregnancy, Delivery, and Child Follow-Up Collected follow-up information includes: β-hCG kinetics, early ultrasound development, and pregnancy complications; delivery outcomes and newborn characteristics; longitudinal developmental assessments of the child up to 3 years of age. Study Objectives To construct digital twin representations of individual blastocysts by integrating multi-omics and clinical parameters obtained during IVF. To identify non-invasive biomarkers of implantation success and embryo viability. To analyze associations between early embryo molecular profiles and neonatal or early childhood developmental outcomes. Study Design This is a non-interventional, observational study. All data are obtained retrospectively and/or prospectively from routine clinical practice in IVF clinics. No experimental procedures, investigational drugs, or investigational devices are introduced. Participation involves only the use of fully de-identified clinical, laboratory, and follow-up data for research purposes. Parents provide informed consent for use of de-identified information. The study is not conducted under an IND or IDE, and it does not involve FDA-regulated products. Significance The resulting longitudinal dataset will support the development of AI-based digital twin models, facilitate biomarker discovery, and advance precision reproductive medicine. These models aim to predict blastocyst competence, implantation potential, and early developmental trajectories using non-image, multimodal clinical and molecular data.

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