ClinicalTrials.gg
Status unknownNCT05699850AI in ARTUpdated Jan 26, 2023

ARTIFICIAL INTELLIGENCE IN REPRODUCTIVE MEDICINE

An observational study in ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE, sponsored by Al Baraka Fertility Hospital. Status unknown at 2 sites in Egypt. Per ClinicalTrials.gov, last updated 2023-01-26.

Sponsored by Al Baraka Fertility Hospital · Observational

The sponsor has not verified this record recently (last verified Jan 2023), so the status shown — last known as Recruiting — may be out of date.
Study type
Observational
Model
Other
Time perspective
Other
Enrollment
10
Sex
All
01

Study summary

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.

Read the 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.

02

Conditions studied

  • ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE

Keywords

  • ARTIFICIAL INTELLIGENCE (AI)
  • IVF
  • ART
03

In context

Lead sponsor

Al Baraka Fertility Hospital is the lead sponsor of 8 studies on the registry; none are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Non-probability sample

Study population

those people coming for fertility clinics

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • fertility related

Exclusion criteria

Exclusion Criteria:

  • fertile people
05

Study design

Observational model
Other
Time perspective
Other
Enrollment
10 participants (estimated)
Patient registry
No

Interventions

  • OtherARTIFICIAL INTELLIGENCE (AI)

    ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE

06

What researchers measure

Primary outcomes

  1. SUCCESS RATE

    ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN REPRODUCTIVE MEDICINE

    Time frame: 4 MONTHS

07

Study locations

2 of 2 sites recruiting
  • Al-Azhar university
    Cairo, 12358, Egypt
    Recruiting
  • Kamal Eldin Abdalla Rageh
    Cairo, 15006, Egypt
    Recruiting
08

References and documents

Individual participant data

Plan to share: Undecided — will think about it

No publications or documents are linked to this record.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Started Nov 2022 (actual). Primary completion is estimated for Mar 2023, 4 months after the start.

How these figures are worked out, and which studies are left out of them.

Registered
Jan 17, 2023
First posted
Jan 26, 2023
StartActual
Nov 1, 2022
Primary completionEst.
Mar 1, 2023
CompletionEst.
Apr 1, 2023
Last update
Jan 26, 2023
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Jan 26, 2023, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT05699850
Lead sponsor
Al Baraka Fertility Hospital
Collaborators
Al-Azhar University
Responsible party
Dr. Kamal Rageh, MD (consultant Doctor, Al-Azhar University) — Principal investigator
First posted
Jan 26, 2023
Start date
Nov 1, 2022
Primary completion
Mar 1, 2023 (estimated)
Completion
Apr 1, 2023 (estimated)
Last update
Jan 26, 2023

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is status unknown, as verified in Jan 2023. You cannot join it, but the record below documents what was studied.

Follow this study

Get an email when the registry record changes — status, dates, results — or when someone posts here.

Sign in to follow

Discussion

Questions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.

Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.

Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.

Start the discussion