Ignite Creation Date:
2025-12-24 @ 5:06 PM
Ignite Modification Date:
2025-12-27 @ 1:40 AM
Study NCT ID:
NCT05699850
Status:
UNKNOWN
Last Update Posted:
2023-01-26
First Post:
2023-01-17
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
ARTIFICIAL INTELLIGENCE IN REPRODUCTIVE MEDICINE
Sponsor:
Al Baraka Fertility Hospital
Organization:
Study Overview
Official Title:
ARTIFICIAL INTELLIGENCE APPLICATIONS IN REPRODUCTIVE MEDICINE
Status:
UNKNOWN
Status Verified Date:
2023-01
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AI in ART
Brief 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.
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.
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 Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: