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 on a new client portal and database for an existing trained model with algorithm that analyzes embryo images and showcase predictive tool to assess the embryo's viability.

The portal should include:

1) Secure login with password encryption

2) Create database to store patient information

3) Access embryo component data and output corresponding results in the predictive tool.

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

Students should be prepared to:

• Create a design proposal including wireframes or mockups for our company's review.
• Build fully-functioning portal, with our assistance in providing content and copy.
• Add new UI and features to improve the portal experience
• Provide documentation on updating and maintaining the portal.
• Provide final presentation on powerpoint on features of the portal.

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