## Inspiration

The California wildfires have proven how deadly fires can be; the mere smoke from fireworks can set ablaze hundreds of acres. What starts as a few sparks can easily become the ignition for a fire capable of destroying homes and habitats. California is just one example; fires can be just as dangerous in other parts of the world, even if not as often.

Approximately 300,000 were affected by fires and 14 million people were affected by floods last year in the US alone. These numbers will continue to rise, due to issues such as climate change.

Preventative equipment and forecasting is only half of the solution; the other half is education. People should be able to navigate any situation they may encounter. However, there are inherent shortcomings in the traditional teaching approach, and our game -S.O.S. - looks to bridge that gap by mixing fun and education.

## What it does

S.O.S. is a first-person story mode game that allows the player to choose between two scenarios: a home fire, or a flooded car. Players will be presented with multiple options designed to either help get the player out of the situation unscathed or impede their escape. For example, players may choose between breaking open car windows in a flood or waiting inside for help, based on their experience and knowledge.

Through trial and error and "bulletin boards" of info gathered from national institutions, players will be able to learn about fire and flood safety. We hope to make learning safety rules fun and engaging, straying from conventional teaching methods to create an overall pleasant experience and ultimately, save lives.

## How we built it

The game was built using C#, Unity, and Blender. Some open resource models were downloaded and, if needed, textured in Blender. These models were then imported into Unity, which was then laid out using ProBuilder and ProGrids. Afterward, C# code was written using the built-in Visual Studio IDE of Unity.

## Challenges we ran into

Some challenges we ran into include learning how to use Unity and code in C# as well as texture models in Blender and Unity itself. We ran into problems such as models not having the right textures or the wrong UV maps, so one of our biggest challenges was troubleshooting all of these problems. Furthermore, the C# code proved to be a challenge, especially with buttons and the physics component of Unity. Time was the biggest challenge of all, forcing us to cut down on our initial idea.

## Accomplishments that we're proud of

There are many accomplishments we as a team are proud of in this hackathon. Overall, our group has become much more adept with 3D software and coding.

## What we learned

We expanded our knowledge of making games in Unity, coding in C#, and modeling in Blender.

## What's next for SOS; Saving Our Souls

Next, we plan to improve the appearance of our game. The maps, lighting, and animation could use some work. Furthermore, more scenarios can be added, such as a Covid-19 scenario which we had initially planned.
## Inspiration

Our game stems from the current global pandemic we are grappling with and the importance of getting vaccinated. As many of our loved ones are getting sick, we believe it is important to stress the effectiveness of vaccines and staying protected from Covid in a fun and engaging game.

## What it does

An avatar runs through a school terrain while trying to avoid obstacles and falling Covid viruses. The player wins the game by collecting vaccines and accumulating points, successfully dodging Covid, and delivering the vaccines to the hospital.
Try out our game by following the link to github!

## How we built it

After brainstorming our game, we split the game components into 4 parts for each team member to work on. Emily created the educational terrain using various assets, Matt created the character and its movements, Veronica created the falling Covid virus spikes, and Ivy created the vaccines and point counter. After each of the components were made, we brought it all together, added music, and our game was completed.

## Challenges we ran into

As all our team members had never used Unity before, there was a big learning curve and we faced some difficulties while navigating the new platform.
As every team member worked on a different scene on our Unity project, we faced some tricky merge conflicts at the end when we were bringing our project together.

## Accomplishments that we're proud of

We're proud of creating a fun and educational game that teaches the importance of getting vaccinated and avoiding Covid.

## What we learned

For this project, it was all our first time using the Unity platform to create a game. We learned a lot about programming in C# and the game development process. Additionally, we learned a lot about git management through debugging and resolving merge conflicts.

## What's next for CovidRun

We want to especially educate the youth on the importance of vaccination, so we plan on introducing the game into k-12 schools and releasing the game on steam. We would like to add more levels and potentially have an infinite level that is procedurally generated.
## Inspiration

DermaDetect was born out of a commitment to improve healthcare equity for underrepresented and economically disadvantaged communities, including seniors, other marginalized populations, and those impacted by economic inequality.

Recognizing the prohibitive costs and emotional toll of traditional skin cancer screenings, which often result in benign outcomes, we developed an open-source AI-powered application to provide preliminary skin assessments.

This innovation aims to reduce financial burdens and emotional stress, offering immediate access to health information and making early detection services more accessible to everyone, regardless of their societal status.

## What it does

* AI-powered analysis: Fine-tuned Resnet50 Convolutional Neural Network classifier that predicts skin lesions as benign versus cancerous by leveraging the open-source HAM10000 dataset.
* Protecting patient data confidentiality: Our application uses OAuth technology (Clerk and Convex) to authenticate and verify users logging into our application, protecting patient data when users upload images and enter protected health information (PHI).
* Understandable and age-appropriate information: Prediction Guard LLM technology offers clear explanations of results, fostering informed decision-making for users while respecting patient data privacy.
* Journal entry logging: Using the Convex backend database schema allows users to make multiple journal entries, monitor their skin, and track moles over long periods.
* Seamless triaging: Direct connection to qualified healthcare providers eliminates unnecessary user anxiety and wait times for concerning cases.

## How we built it

**Machine learning model**

TensorFlow, Keras: Facilitated our model training and model architecture, Python, OpenCV, Prediction Guard LLM, Intel Developer Cloud, Pandas, NumPy, Sklearn, Matplotlib

**Frontend**

TypeScript, Convex, React.js, Shadcn (Components), FramerMotion (Animated components), TailwindCSS

**Backend**

TypeScript, Convex Database & File storage, Clerk (OAuth User login authentication), Python, Flask, Vite, InfoBip (Twillio-like service)

## Challenges we ran into

* We had a lot of trouble cleaning and applying the HAM10000 skin images dataset. Due to long run times, we found it very challenging to make any progress on tuning our model and sorting the data. We eventually started splitting our dataset into smaller batches and training our model on a small amount of data before scaling up which worked around our problem. We also had a lot of trouble normalizing our data, and figuring out how to deal with a large Melanocytic nevi class imbalance. After much trial and error, we were able to correctly apply data augmentation and oversampling methods to address the class imbalance issue.
* One of our biggest challenges was setting up our backend Flask server. We encountered so many environment errors, and for a large portion of the time, the server was only able to run on one computer. After many Google searches, we persevered and resolved the errors.

## Accomplishments that we're proud of

* We are incredibly proud of developing a working open-source, AI-powered application that democratizes access to skin cancer assessments.
* Tackling the technical challenges of cleaning and applying the HAM10000 skin images dataset, dealing with class imbalances, and normalizing data has been a journey of persistence and innovation.
* Setting up a secure and reliable backend server was another significant hurdle we overcame. The process taught us the importance of resilience and resourcefulness, as we navigated through numerous environmental errors to achieve a stable and scalable solution that protects patient data confidentiality.
* Integrating many technologies that were new to a lot of the team such as Clerk for authentication, Convex for user data management, Prediction Guard LLM, and Intel Developer Cloud.
* Extending beyond the technical domain, reflecting a deep dedication to inclusivity, education, and empowerment in healthcare.

## What we learned

* Critical importance of data quality and management in AI-driven applications. The challenges we faced in cleaning and applying the HAM10000 skin images dataset underscored the need for meticulous data preprocessing to ensure AI model accuracy, reliability, and equality.
* How to Integrate many different new technologies such as Convex, Clerk, Flask, Intel Cloud Development, Prediction Guard LLM, and Infobip to create a seamless and secure user experience.

## What's next for DermaDetect

* Finding users to foster future development and feedback.
* Partnering with healthcare organizations and senior communities for wider adoption.
* Continuously improving upon data curation, model training, and user experience through ongoing research and development.