Multidimensional trophoblast invasion assessment by combining 3D in vitro modeling and deep learning analysis

Area of Science:

• Reproductive Biology
• Biomedical Engineering
• Computational Biology

Background:

• Infertility affects millions globally, with in vitro fertilization (IVF) being a primary treatment.
• High failure rates (~60%) in first IVF attempts necessitate improved research tools and therapeutic strategies.
• Current limitations exist in studying embryo implantation, drug testing, and quantitative image analysis for IVF.

Purpose of the Study:

• To develop an automated, quantitative tool for assessing trophoblast invasion in embryo implantation.
• To create a Fiji plugin, ImplantoMetrics, integrating a 3D in-vitro model with advanced computational methods.
• To enhance the study of implantation processes and evaluate therapeutic interventions in vitro.

Main Methods:

• Development of ImplantoMetrics, a Fiji plugin utilizing Convolutional Neural Networks (CNN) and XGBoosting algorithms.
• Quantitative assessment of multidimensional trophoblast expansion patterns using the developed plugin.
• Integration with a 3D in-vitro model for studying embryo implantation dynamics.

Main Results:

• ImplantoMetrics accurately measures trophoblast invasion and multidimensional expansion patterns.
• The tool enables quantitative evaluation of therapeutic interventions in in-vitro settings.
• Automated analysis by ImplantoMetrics is approximately 13 times faster than manual methods and reduces errors.

Conclusions:

• ImplantoMetrics provides a robust, automated solution for quantitative analysis of trophoblast invasion in IVF research.
• The plugin facilitates complex studies of implantation and accelerates the development of new fertility treatments.
• ImplantoMetrics is adaptable for studying spheroid invasion in other biological contexts, including cancer research.