Longitudinal Clinical Observation of a Digital Twin Model for Blastocyst Evaluation in IVF Clinics
NCT ID: NCT07305480
Last Updated: 2025-12-26
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
1 participants
OBSERVATIONAL
2023-01-02
2026-12-15
Brief Summary
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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.
Detailed Description
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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.
Conditions
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Keywords
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Study Design
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COHORT
OTHER
Study Groups
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Cohort: IVF Patients Monitored With Digital Twin Embryo Evaluation
This cohort includes patients undergoing in-vitro fertilization (IVF) whose embryos are evaluated using the Digital Twin model for blastocyst quality and implantation prediction. No clinical intervention is performed. The study collects multi-omics, morphological, and clinical data to monitor implantation success, pregnancy progression, and neonatal outcomes up to 3 years after birth.
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
Availability of non-image embryo development data, including:
text-based morphological descriptions,
PGT-A results,
secretome and exosomal biomarkers,
molecular / biochemical profiles,
IVF cycle parameters collected during routine clinical workflow.
Embryos evaluated according to standard clinic protocols, with documented implantation outcomes (positive or negative).
Age of the oocyte provider between 20 and 42 years.
Signed informed consent allowing the use of fully de-identified clinical, laboratory, molecular, and follow-up data for development and validation of the digital twin computational model.
Exclusion Criteria
Use of donor oocytes or donor embryos when linkage with necessary clinical or laboratory metadata is not possible.
Cases in which implantation outcome cannot be confirmed (e.g., lost to follow-up).
Presence of severe uterine abnormalities prior to embryo transfer (e.g., untreated intrauterine adhesions, large submucosal fibroids) making implantation outcome unreliable for algorithmic validation.
Withdrawal of consent for the use of anonymized clinical, laboratory, or follow-up data.
ALL
No
Sponsors
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Ukraine Association of Biobank
OTHER
Responsible Party
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Locations
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Ukrainian Association of Biobanks Austria - Digital Twin Lab
Graz, , Austria
Countries
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Other Identifiers
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UABA-IVF-DT-001
Identifier Type: REGISTRY
Identifier Source: secondary_id
IVFDT-LONGOBS-001
Identifier Type: -
Identifier Source: org_study_id