- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05699850
ARTIFICIAL INTELLIGENCE IN REPRODUCTIVE MEDICINE (AI in ART)
ARTIFICIAL INTELLIGENCE APPLICATIONS IN REPRODUCTIVE MEDICINE
Study Overview
Status
Intervention / Treatment
Detailed Description
ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE
Kamaleldin Abdullah Rageh, M.D. (1).
Mohammad Atef Behery, M.D. (2)
Elsayed Ali Farag, M.D. (1)
1 -Department of Obstetrics and Gynecology, Faculty of medicine, Al-Azhar University, Cairo, Egypt.
2-International Islamic Center for Population Studies and Research, Al-Azhar University, Cairo, Egypt.
Abstract:
In spite of improved almost all aspects of IVF: ovarian stimulation, embryo culture and transfer, the pregnancy rates still not satisfactory. Studies confirm that up to 50% of the performed IVF cycles fail and there may be no direct explanation for this.
And it's worthy to mention that accurately predicting the outcome of an IVF cycle has yet to be achieved. One reason for this is the method of selecting an embryo for transfer. Morphological assessment of embryos is the traditional method of evaluating embryo quality and selecting which embryo to transfer. However, this subjective method of assessing embryos leads to inter- and intra-observer variability, resulting in less than optimal IVF success rates. Although time-lapse incubators and preimplantation genetic testing for aneuploidy have been introduced to help increase the chances of live birth, the outcomes remain less than ideal.
Currently, infertility treatments exert a lot of financial and emotional stress, especially in patients with previously failed IVF treatments, where there is no clear cause to be identified is a common, heartbreaking endpoint when the emotional, financial and physical burden of the treatment escalate to continue finding answers, but AI systems might help solve the dilemma by picking the best viable embryos that humans can't do. AI technologies have excellent potential to help the infertility field to soar over its current narrow focus on individual embryos and detect new patterns hidden in the patient data for overcoming the prevailing infertility cases.
The embryo selection is the most critical factor for the success of IVF. However, there is no single definitive criterion that can predict the success of an embryo. Rather, embryo selection is based on a variety of factors, making it is difficult to predict the probability of a successful pregnancy for each patient and to fully understand the cause of each failure. So, Utilization of artificial intelligence (AI) may support the clinicians in filling this knowledge gap, thereby being leveraged in the embryology laboratory to help improve IVF outcomes.
Study Type
Enrollment (Anticipated)
Contacts and Locations
Study Locations
-
-
-
Cairo, Egypt, 12358
- Recruiting
- Al-Azhar University
-
Contact:
- Dr. Mohammed Atef, M.D.
- Phone Number: 00201006206040
- Email: kimovip2000@yahoo.com
-
Cairo, Egypt, 15006
- Recruiting
- Kamal Eldin Abdalla Rageh
-
Contact:
- Kamal Eldin A Rageh
- Phone Number: 0097333153871
- Email: dr_kamal_rageh@yahoo.com
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- fertility related
Exclusion Criteria:
- fertile people
Study Plan
How is the study designed?
Design Details
- Observational Models: Other
- Time Perspectives: Other
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
SUCCESS RATE
Time Frame: 4 MONTHS
|
ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE
|
4 MONTHS
|
Collaborators and Investigators
Sponsor
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Estimate)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
- Kamal-AI
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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