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Longitudinal Clinical Observation of a Digital Twin Model for Blastocyst Evaluation in IVF Clinics
Sponsor: Ukraine Association of Biobank
Summary
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.
Official title: Longitudinal Observational Study for Developing and Validating a Digital Twin Model of Human Blastocyst Development, Implantation Potential, and Pregnancy Outcomes Using Fully De-Identified, Multimodal IVF Clinical and Molecular Data
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
1
Start Date
2023-01-02
Completion Date
2026-12-15
Last Updated
2025-12-29
Healthy Volunteers
No
Conditions
Interventions
Digital Twin Computational Modeling
Computational digital twin model that analyzes fully de-identified, non-image clinical, molecular, biochemical, and laboratory data from routine IVF care to evaluate embryo implantation potential. The model does not influence clinical decision-making and is used only for retrospective and prospective observational analysis.
Locations (1)
Ukrainian Association of Biobanks Austria - Digital Twin Lab
Graz, Austria