Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06539104
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-01
Brief Title:
Aftificial Inteligence in Assisted Reproductive Techniques to Assess Oocyte Quality and Embryo Ploidy
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Scanning the Meiotic Spindle in Assisted Reproductive Techniques to Assess Oocyte Quality and Embryo Ploidy Evaluated by Artificial Intelligence SMARTAI Study
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
SMARTAI
Brief Summary:
The assisted reproduction success rate is affected by several factors including the age of the women oocyte quality and maturation state as well as sperm quality Imaging of the meiotic spindle may be crucial for determining the oocyte maturation Artificial intelligence AI will be applied to establish the complex oocyte quality embryo ploidy and pregnancy success probability from the sequence of data starting with the recording of the meiotic spindle in polarized light through paternal factors up to the time lapse recording of early embryo development This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses
Detailed Description:
One of the main strategies of infertility treatment is in vitro fertilization IVF The IVF success rate is affected by several key factors including the age of the women oocyte quality and maturation state as well as sperm quality It has been suggested that the presence position and retardance of the optically birefringent meiotic spindle MS are related to oocyte developmental competence affecting the quality of fertilization and embryo development Artificial intelligence AI will be applied to establish the complex oocyte quality embryo ploidy and pregnancy success probability from the sequence of data starting with the recording of the meiotic spindle in polarized light through paternal factors up to the time lapse recording of early embryo development
Synergic approaches will be used to increase the quality of embryos for implantation image analysis and machine learning techniques will be applied to the oocyte microscopic images to perform the MS analysis fully automatically and to determine whether some other aspects of the oocyte appearance might correlate with the optimal timing and fertilization and pregnancy success or genetic defects An automatic method of embryo evaluation based on time-lapse videos after ICSI and MS imaging plus other scalar parameters extracted features can be used as inputs for the downstream tasks eg features extracted from oocytes and sperm can serve as additional inputs to the embryo classifier will be used This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses
The analysis will be performed in cooperation with Czech Technical University and Institute of Physics Academy of Sciences of the Czech Republic who will create a software tool capable of predicting the probability of pregnancy and embryo ploidy status from oocyte images plus time-lapse video of a developing embryo after ICSI It will be determined whether some other aspects of the oocyte appearance correlate with the fertilization and pregnancy success or genetic defects
Time lapse sequences of embryonic development and oocyte images will be acquired from VFN and from cooperating IVF centers Gynem sro Repromeda sro The sequences will be stored and paired with outcome ploidy status pregnancy and also with previously acquired oocyte images BIOCEV Academy of sciences of the Czech Republic will evaluate sperm parameters with respect to oocyte fertilization rate and early embryonic development
Synergic approaches will be used to increase the quality of embryos for implantation image analysis and machine learning techniques will be applied to the oocyte microscopic images to perform the MS analysis fully automatically and to determine whether some other aspects of the oocyte appearance might correlate with the optimal timing and fertilization and pregnancy success or genetic defects An automatic method of embryo evaluation based on time-lapse videos after ICSI and MS imaging plus other scalar parameters extracted features can be used as inputs for the downstream tasks eg features extracted from oocytes and sperm can serve as additional inputs to the embryo classifier will be used This strategy should reduce the cost of fertility treatment thanks to increased efficiency in choosing the most promising candidates and reducing the need for costly laboratory analyses
The analysis will be performed in cooperation with Czech Technical University and Institute of Physics Academy of Sciences of the Czech Republic who will create a software tool capable of predicting the probability of pregnancy and embryo ploidy status from oocyte images plus time-lapse video of a developing embryo after ICSI It will be determined whether some other aspects of the oocyte appearance correlate with the fertilization and pregnancy success or genetic defects
Time lapse sequences of embryonic development and oocyte images will be acquired from VFN and from cooperating IVF centers Gynem sro Repromeda sro The sequences will be stored and paired with outcome ploidy status pregnancy and also with previously acquired oocyte images BIOCEV Academy of sciences of the Czech Republic will evaluate sperm parameters with respect to oocyte fertilization rate and early embryonic development
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None