Inspiration

Increasing accessibility: Connecting donors with clinics and labs expands the options for families seeking IVF.

Enhancing embryo selection: The AI platform aims to improve embryo selection, a critical step in IVF success.

Leveraging expertise: The focus on collaboration between embryologists and AI suggests the platform aims to support, not replace, human specialists.

What it does

Connects Donors and Clinics: It acts as a platform to connect individuals or couples seeking egg or sperm donation (donors) with IVF clinics, hospitals, and laboratories. This could streamline the process for families by providing a centralized location to find compatible donors and qualified facilities.

Provides Secure AI Platform: The project involves creating a secure platform that utilizes Artificial Intelligence (AI). This platform likely:

Links Clinics with Embryologists: It facilitates communication and collaboration between IVF clinics and embryologists, potentially allowing for sharing of expertise and resources. Assists with Embryo Selection: The AI component might analyze data and provide insights to embryologists during the crucial process of selecting viable embryos for implantation.

How we built it

Data Acquisition and Processing: Collecting a large dataset of labeled embryo images and corresponding success/failure data from IVF clinics. Cleaning and pre-processing the data for AI model training.

AI Model Development: Choosing and training a machine learning model (e.g., deep learning convolutional neural network) to analyze embryo images and identify features associated with successful implantation. Fine-tuning the model to improve its accuracy in predicting embryo viability.

Platform Development: Building a secure platform that integrates with existing IVF clinic systems. Designing a user interface for embryologists to interact with the AI model and view its recommendations. Implementing security measures to protect sensitive patient data.

Validation and Testing: Rigorously testing the AI model's performance on unseen data to ensure its generalizability and effectiveness. Gathering feedback from embryologists during the testing phase to refine the platform's functionality and user experience.

Ongoing Development: Continuously collecting new data to improve the AI model's performance over time. Adding new features and functionalities based on user feedback and advancements in AI technology.

Challenges we ran into

Data Acquisition: Training a robust AI for embryo selection requires a massive dataset of labeled images and information about embryos and their outcomes. Obtaining sufficient high-quality data from IVF clinics can be challenging due to privacy concerns and ethical considerations.

AI Model Development: Designing an AI model that can accurately analyze complex biological structures like embryos and predict their viability is a significant technical hurdle. Integration with Existing Workflows: Implementing a new AI platform in established clinical settings requires careful integration with existing workflows and ensuring seamless adoption by embryologists.

Regulatory hurdles: The use of AI in embryo selection is a novel technology and may face regulatory scrutiny to ensure safety and effectiveness before widespread adoption.

Ethical Considerations: There are ethical concerns surrounding the use of AI in embryo selection, such as potential bias in the algorithms and the role it might play in designer babies.

Accomplishments that we're proud of

Innovation: Developing a secure AI platform specifically for the IVF industry is a novel approach.

Collaboration: Creating a system that bridges the gap between AI technology and embryologist expertise demonstrates a commitment to a collaborative approach.

Accessibility focus: If the platform helps connect donors and clinics, it could be making IVF more accessible to families.

Technical milestones: Completion of core functionalities of the AI platform or successful integration with existing IVF clinic systems.

Early testing results: This platform has undergone initial testing with promising results on embryo selection accuracy, that could be a significant accomplishment.

Partnerships: Building partnerships with IVF clinics or embryologist associations demonstrates industry recognition and validation.

What we learned

Challenges of connecting donors and clinics: There might have been learnings about the logistics and regulations involved in facilitating connections between donors, clinics, and labs.

Effectiveness of AI in embryo selection: The project might be in the early stages, but initial trials could reveal insights into the strengths and limitations of AI-assisted embryo selection. Importance of collaboration between AI and embryologists: Perhaps the project has learned that the best approach involves a combination of AI analysis and the expertise of human embryologists. To get a more accurate understanding of what the project has learned, we would likely need additional information such as:

Project stage: Is it in the initial development phase, or are there already trials ongoing? Data collected: Has the project gathered data on user experiences, embryo selection outcomes, or AI performance?

Project goals: Were there specific learning objectives outlined at the project's start?

What's next for DNA Stash

Improved AI algorithms: AI is constantly evolving, and future iterations may become even more adept at analyzing embryos and predicting implantation potential. Integration with genetic testing: AI could be combined with preimplantation genetic testing (PGT) to identify embryos with a higher chance of successful implantation and minimize the risk of genetic disorders.

Standardization and validation: As AI becomes more established, standardization and rigorous validation of these technologies will be crucial for wider adoption and trust within the medical community.

Ethical considerations: As AI plays a larger role, discussions around ethical considerations such as transparency, bias mitigation, and human oversight will be important.

Built With

• css3
• generativeai
• google-cloud-sql
• javascript
• llm
• node.js
• react
• react-native
• vertexai