Clinical trial NCT06408389: Mechanics of Human Pre Implantation Development
Official title: From Oocyte to Embryo: Analysis of Mechanics of Human Pre Implantation Development
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Infertility
Keywords: In vitro fertilization
embryo
human time lapse
pregnancy
delivery
machine learning
Lead sponsor: Assistance Publique - Hôpitaux de Paris
Sponsor class: OTHER
Collaborators: name: Collège de France
name: Institut Curie
name: Centre National de la Recherche Scientifique, France
First posted: 2024-05-10
Enrollment: 1500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 43
Standard ages: ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Couples enrolled in an IVF process (conventional IVF or with sperm micro-injection (ICSI))

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.

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\> 0.98. 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 IVF/ICSI using machine learning algorithms and relate these morpho-kinetic factors to the mechanical characteristics of cells.

Eligibility criteria: Inclusion Criteria:

* Couples enrolled in an IVF process and with embryos cultured in time-lapse
* Couples informed non opposed to research

Exclusion Criteria:

* Couples opposed to research
* Couples under curator or tutorship
* Couples under state xxx

Primary outcomes:
measure: Prediction of embryo implantation by the machine learning algorithm from embryo morphokinetic parameters and patient health data.
timeFrame: 2 years
Secondary outcomes:
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Link between couples' parameters and embryo morpho-kinetics
timeFrame: 3 years
measure: Statistical correlation between embryo implantation prediction and morphokinetic parameters
timeFrame: 3 years
measure: Embryo reconstruction in 3D
timeFrame: 3 years
measure: Software developing
timeFrame: 3 years
Other outcomes:
Locations:
Study officials:
affiliation: AP-HP (Professor in Medicine faculty and Hospital practitioner (APHP.centre - Université de Paris, site Cochin)
name: Catherine PATRAT, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Institut Curie CNRS UMR3215 INSERM U934
name: Jean-Léon MAITRE, PhD
role: STUDY_DIRECTOR
affiliation: Collège de France, CIRB CNRS UMR7241
name: Hervé TURLIER
role: STUDY_DIRECTOR
References: