ARTIFICIAL INTELLIGENCE IN REPRODUCTIVE MEDICINE (AI in ART)

January 17, 2023 updated by: Dr. Kamal Rageh, MD, Al Baraka Fertility Hospital

ARTIFICIAL INTELLIGENCE APPLICATIONS IN REPRODUCTIVE MEDICINE

Many studies have been published investigating the use of AI as an unbiased, automated approach to embryo assessment. This review will summarize the recent AI advancements in the IVF field. Hopefully, that incorporating AI technology into the IVF clinics may be the next frontier in the journey towards personalised reproductive medicine and improved fertility outcomes for patients.

Study Overview

Status

Recruiting

Conditions

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

Observational

Enrollment (Anticipated)

10

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Egypt
      • Cairo, Egypt, 12358
        • Recruiting
        • Al-Azhar University
        • Contact:
      • Cairo, Egypt, 15006
        • Recruiting
        • Kamal Eldin Abdalla Rageh
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

those people coming for fertility clinics

Description

Inclusion Criteria:

  • fertility related

Exclusion Criteria:

  • fertile people

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

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

This is where you will find people and organizations involved with this study.

Sponsor

Al Baraka Fertility Hospital

Collaborators

Al-Azhar University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 1, 2022

Primary Completion (Anticipated)

March 1, 2023

Study Completion (Anticipated)

April 1, 2023

Study Registration Dates

First Submitted

January 17, 2023

First Submitted That Met QC Criteria

January 17, 2023

First Posted (Estimate)

January 26, 2023

Study Record Updates

Last Update Posted (Estimate)

January 26, 2023

Last Update Submitted That Met QC Criteria

January 17, 2023

Last Verified

January 1, 2023

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)?

Undecided

IPD Plan Description

will think about it

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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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