An Advanced Decision Support Tool for Personalized Medicine for IVF Using Modeling and Optimization
NCT ID: NCT05377879
Last Updated: 2022-07-21
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
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
COMPLETED
NA
44 participants
INTERVENTIONAL
2022-05-10
2022-07-01
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Opt-IVF Multi-center Trial 2 Including All Protocols
NCT05981898
The Effectiveness of Advanced Decision Support Tool (OPT-IVF) for IVF Treatment
NCT06179420
Tamoxifen and Clomiphene Citrate in Mild Stimulation IVF
NCT02690870
Endometrial Receptivity Prediction During in Vitro Fertilization Using Artificial Intelligence
NCT06717802
SprmPik AI Sperm Selection Study SiD, an Assistant for Sperm Selection During Intracytoplasmic Sperm Injection in Medically Assisted Reproduction: Effect on Fertilization, Blastocyst Formation, Early Pregnancy Loss, and Consistent Practice. A Prospective Pilot Study.
NCT06518928
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
The main site for this task is the Akansha Hospital, India, and all the analysis will be carried out at the Stochastic Research site. Dr. Urmila Diwekar will be an investigator from the Stochastic Research Technologies LLC, and Dr. Nayana Patel will be an investigator from Akansha Hospital and Research Institute.
In our study, the investigators will be using the participant's age and day three serum day AMH and FSH levels to decide the starting dose for the patient's cycle. The investigators will use the first two days of data collected (Follicular size distribution, estrogen levels) for that paticipant to determine the optimal dosage profile for the entire cycle for that participant with the help of the decision support tool OPT-IVF for this intervention in the clinical trial.
Primary and secondary outcomes will be measured.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
NON_RANDOMIZED
PARALLEL
OTHER
DOUBLE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
OPTIVF predicted drug dosage
In our study, we will be using the patient's age and day three serum day AMH and FSH levels to decide the starting dose for the patient's cycle. We will use the first two days of data collected (Follicular size distribution, estrogen levels) for that patient to determine the optimal dosage profile for the entire cycle for that patient with the help of the decision support tool OPTIVF for this intervention in the clinical trial.
OPT-IVF dosage
Dosage predicted for each patient by the decision support tool OPTIVF
Traditional drug treatment
Patients where drug dosage is decided by the physician based on ultrasound and estrogen levels for each day of the cycle.
No interventions assigned to this group
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
OPT-IVF dosage
Dosage predicted for each patient by the decision support tool OPTIVF
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
Exclusion Criteria
20 Years
50 Years
FEMALE
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Akansha Hospital and Research Institute, India
UNKNOWN
Stochastic Research Technologies LLC
INDUSTRY
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Urmila Diwekar, Ph.D.
Role: PRINCIPAL_INVESTIGATOR
Stochastic Research Technologies/University of Illinois at Chicago
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Akansha Hospital and Research Institute
Anand, Gujarat, India
Countries
Review the countries where the study has at least one active or historical site.
References
Explore related publications, articles, or registry entries linked to this study.
Nisal A, Diwekar U, Bhalerao V. Personalized medicine for in vitro fertilization procedure using modeling and optimal control. J Theor Biol. 2020 Feb 21;487:110105. doi: 10.1016/j.jtbi.2019.110105. Epub 2019 Dec 3.
Yenkie KM, Diwekar UM, Bhalerao V. Modeling the superovulation stage in in vitro fertilization. IEEE Trans Biomed Eng. 2013 Nov;60(11):3003-8. doi: 10.1109/TBME.2012.2227742. Epub 2012 Nov 15.
Yenkie KM, Diwekar U. Uncertainty in clinical data and stochastic model for in vitro fertilization. J Theor Biol. 2015 Feb 21;367:76-85. doi: 10.1016/j.jtbi.2014.11.004. Epub 2014 Dec 4.
Related Links
Access external resources that provide additional context or updates about the study.
description is available at http://www.stochastic-research.com/InVitroFertilization.html
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
opivf-protocol-22-1
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.