From Oocyte to Embryo: Analysis of Mechanics of Human Pre Implantation Development
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 1,500
- 主要终点
- Prediction of embryo implantation by the machine learning algorithm from embryo morphokinetic parameters and patient health data.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 43 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Couples enrolled in an IVF process and with embryos cultured in time-lapse
- •Couples informed non opposed to research
排除标准
- •Couples opposed to research
- •Couples under curator or tutorship
- •Couples under state xxx
结局指标
主要结局
Prediction of embryo implantation by the machine learning algorithm from embryo morphokinetic parameters and patient health data.
时间窗: 2 years
The algorithm outcome will be evaluated retrospectively on human embryos which have been transferred, for which we know whether its implantation was successful and led to birth. Using embryo morphokinetic and health patient data, we will predict a probability of implantion and compare its value (\<0.5: no implantation or \>0.5: implantation) to the true result of the embryo transfer (no implantation or implantation). This will allow us to evaluate the potential of the algorithm to support clinical decision-making in the future.
次要结局
- Link between couples' parameters and embryo morpho-kinetics(3 years)
- Software developing(3 years)
- Statistical correlation between embryo implantation prediction and morphokinetic parameters(3 years)
- Embryo reconstruction in 3D(3 years)