In-vitro fertilization (IVF), as the most common fertility treatment, has never reached its maximum potentials. Systematic selection of embryos with the highest implementation potentials is a necessary step toward enhancing the effectiveness of IVF. The embryologist evaluates embryo viability by manual microscopic assessment of its components. With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human embryos for computerized analysis. Manual assessment of embryos components is a crucial task that involves careful observation by embryologists, and this process can be automated using artificial intelligence (AI)-based algorithms. In this project, students are to work with an existing trained model with algorithm that analyzes embryo images and create a predictive tool to assess the embryo's viability.

The project framework should be the following:

1) Train model with provided embryo component data and the corresponding results (implanted/not implanted and component grading) to develop the predictive tool.

2) Machine Learning Model (Coded in Python) to accept embryo component data and output the predicted outcome (implanted/not implanted).

3) Update current GUI to report output

Suggestions and technical material will be provided to students through meetings and discussions in coming up with the proper model and code structure.