Ignite Creation Date:
2024-05-11 @ 8:31 AM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06408389
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-05-10
First Post:
2021-11-16
Brief Title:
Mechanics of Human Pre Implantation Development
Sponsor:
Assistance Publique - Hôpitaux de Paris
Organization:
Assistance Publique - Hôpitaux de Paris
Study Overview
Official Title:
From Oocyte to Embryo Analysis of Mechanics of Human Pre Implantation Development
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
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:
MECANEMB
Brief Summary:
The time-lapse is a closed tri-gas incubator of the latest generation that provides optimal and stable culture conditions for the culture of embryos in In Vitro Fertilization IVF The integration of a camera within this incubator allows for continuous image capture thus facilitating the monitoring of the entire embryonic development from the day of fertilization to the moment of transfer into the uterus
The contribution of the time-lapse system allows an evaluation of the embryos not only by their morphology but also by their cell division kinetics both being direct markers of cell mechanics Together these morpho-kinetic data finally allow for the best identification of embryos with greater implantation potential Time-lapse imaging represents a further step towards an objective assessment of the embryo but inter- and intra-embryologist variations in annotations partly compromise this objectivity In addition many decision algorithms based on the evaluation of morpho-kinetic parameters have been developed but the lack of reproducibility from one Assisted Reproductive Technology ART center to another is a hindrance to the generalization of any particular algorithm The aim of this retrospective study is to determine morpho-kinetic factors predictive of implantation using machine learning and to link these factors to human embryo mechanistic properties
The contribution of the time-lapse system allows an evaluation of the embryos not only by their morphology but also by their cell division kinetics both being direct markers of cell mechanics Together these morpho-kinetic data finally allow for the best identification of embryos with greater implantation potential Time-lapse imaging represents a further step towards an objective assessment of the embryo but inter- and intra-embryologist variations in annotations partly compromise this objectivity In addition many decision algorithms based on the evaluation of morpho-kinetic parameters have been developed but the lack of reproducibility from one Assisted Reproductive Technology ART center to another is a hindrance to the generalization of any particular algorithm The aim of this retrospective study is to determine morpho-kinetic factors predictive of implantation using machine learning and to link these factors to human embryo mechanistic properties
Detailed Description:
The time-lapse is a closed tri-gas incubator of the latest generation that provides optimal and stable culture conditions for the culture of embryos in In Vitro Fertilization IVF The integration of a camera within this incubator allows for continuous image capture thus facilitating the monitoring of the entire embryonic development from the day of fertilization to the moment of transfer into the uterus
The contribution of the time-lapse system allows an evaluation of the embryos not only by their morphology but also by their cell division kinetics both being direct markers of cell mechanics Together these morpho-kinetic data finally allow for the best identification of embryos with greater implantation potential Time-lapse imaging represents a further step towards an objective assessment of the embryo but inter- and intra-embryologist variations in annotations partly compromise this objectivity In addition many decision algorithms based on the evaluation of morpho-kinetic parameters have been developed but the lack of reproducibility from one Assisted Reproductive Technology ART center to another is a hindrance to the generalization of any particular algorithm
Machine learning is one of the main methods of data analysis that could define algorithms that are unbiased more robust and applicable to all centers But the optimal algorithm is not yet defined Recently an artificial intelligence approach applied to a large collection of time-lapse embryo images was developed to determine the embryo with the highest grade of evolution with an AUC 098 Using clinical data the authors created a decision tree to integrate embryo quality and female age and identify the chances of pregnancy However this approach did not take into account the whole kinetics of development focusing on certain particular stages nor the influence of parental and extrinsic factors other than age
The aim of this retrospective study is to determine morpho-kinetic factors predictive of implantation and embryo development in IVFICSI using machine learning algorithms and relate these morpho-kinetic factors to the mechanical characteristics of cells
The contribution of the time-lapse system allows an evaluation of the embryos not only by their morphology but also by their cell division kinetics both being direct markers of cell mechanics Together these morpho-kinetic data finally allow for the best identification of embryos with greater implantation potential Time-lapse imaging represents a further step towards an objective assessment of the embryo but inter- and intra-embryologist variations in annotations partly compromise this objectivity In addition many decision algorithms based on the evaluation of morpho-kinetic parameters have been developed but the lack of reproducibility from one Assisted Reproductive Technology ART center to another is a hindrance to the generalization of any particular algorithm
Machine learning is one of the main methods of data analysis that could define algorithms that are unbiased more robust and applicable to all centers But the optimal algorithm is not yet defined Recently an artificial intelligence approach applied to a large collection of time-lapse embryo images was developed to determine the embryo with the highest grade of evolution with an AUC 098 Using clinical data the authors created a decision tree to integrate embryo quality and female age and identify the chances of pregnancy However this approach did not take into account the whole kinetics of development focusing on certain particular stages nor the influence of parental and extrinsic factors other than age
The aim of this retrospective study is to determine morpho-kinetic factors predictive of implantation and embryo development in IVFICSI using machine learning algorithms and relate these morpho-kinetic factors to the mechanical characteristics of cells
Study Oversight
Has Oversight DMC:
None
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?:
None