## 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.
# Inspiration

Meet one of our teammates, Lainey! Over the past three years, she has spent over 2,000 hours volunteering with youth who attend under-resourced schools in Washington state. During the sudden onset of the pandemic, the rapid school closures ended the state’s Free and Reduced Lunch program for thousands of children across the state, pushing the burden of purchasing healthy foods onto parents. It became apparent that many families she worked with heavily relied on government-provided benefits such as SNAP (Supplemental Nutrition Assistance Program) to purchase the bare necessities. Research shows that SNAP is associated with alleviating food insecurity. Receiving SNAP in early life can lead to improved outcomes in adulthood. Low-income families under SNAP are provided with an EBT (Electronic Benefit Transfer) card and are able to load a monthly balance and use it like a debit card to purchase food and other daily essentials.

However, the EBT system still has its limitations: to qualify to accept food stamps, stores must sell food in each of the staple food categories. Oftentimes, the only stores that possess the quantities of scale to achieve this are a small set of large chain grocery stores, which lack diverse healthy food options in favor of highly-processed goods. Not only does this hurt consumers with limited healthy options, it also prevents small, local producers from selling their ethically and sustainably sourced produce to those most in need. Studies have repeatedly shown a direct link between sustainable food production and food health quality.

The primary grocery sellers who have the means and scale to qualify to accept food stamps are large chain grocery stores, which often have varying qualities of produce (correlated with income in that area) that pale compared to output from smaller farms. Additionally, grocery stores often supplement their fresh food options with a large selection of cheaper, highly-processed items that are high in sodium, cholesterol, and sugar. On average, unhealthy foods are about $1.50 cheaper per day than healthy foods, making it both less expensive and less effort to choose those options. Studies have shown that lower income individuals “consume fewer fruits and vegetables, more sugar-sweetened beverages, and have lower overall diet quality”. This leads to deteriorated health, inadequate nutrition, and elevated risk for disease. In addition, groceries stores with healthier, higher quality products are often concentrated in wealthy areas and target a higher income group, making distance another barrier to entry when it comes to getting better quality foods.

Meanwhile, small, local farmers and stores are unable to accept food stamp payments. Along with being higher quality and supporting the community, buying local foods are also better for the environment. Local foods travel a shorter distance, and the structure of events like farmers markets takes away a customer’s dependency on harmful monocrop farming techniques. However, these benefits come with their own barriers as well. While farmers markets accept SNAP benefits, they (and similar events) aren’t as widespread: there are only 8600 markets registered in the USDA directory, compared to the over 62,000 grocery stores that exist in the USA. And the higher quality foods have their own reputation of higher prices.

Locl works to alleviate these challenges, offering a platform that supports EBT card purchases to allow SNAP benefit users to purchase healthy food options from local markets.

# What does Locl do?

Locl works to bridge the gap between EBT cardholders and fresh homegrown produce. Namely, it offers a platform where multiple local producers can list their produce online for shoppers to purchase with their EBT card. This provides a convenient and accessible way for EBT cardholders to access healthy meals, while also promoting better eating habits and supporting local markets and farmers. It works like a virtual farmers market, combining the quality of small farms with the ease and reach of online shopping. It makes it easier for a consumer to buy better quality foods with their EBT card, while also allowing a greater range of farms and businesses to accept these benefits. This provides a convenient and accessible way for EBT cardholders to access healthy meals, while also promoting better eating habits and supporting local markets and farmers.

When designing our product, some of our top concerns were the technological barrier of entry for consumers and ensuring an ethical and sustainable approach to listing produce online. To use Locl, users are required to have an electronic device and internet connection, ultimately limiting access within our target audience. Beyond this, we recognized that certain produce items or markets could be displayed disproportionally in comparison to others, which could create imbalances and inequities between all the stakeholders involved. We aim to address this issue by crafting a refined algorithm that balances the search appearance frequency from a certain product based on how many similar products like such are posted.

# Key Features

## EBT Support

Shoppers can convert their EBT balance into Locl credits. From there, they can spend their credits buying produce from our set of carefully curated suppliers. To prevent fraud, each vendor is carefully evaluated to ensure they sell ethically sourced produce. Thus, shoppers can only spend their Locl credits on produce, adhering to government regulation on SNAP benefits.

## Bank-less payment

Because low-income shoppers may not have access to a bank account, we've used Checkbook.io's virtual credit cards and direct deposit to facilitate payments between shoppers and vendors.

## Producer accessibility

By listing multiple vendors on one platform, Locl is able to circumvent the initial problems of scale. Rather than each vendor being its own store, we consolidate them all into one large store, thereby increasing accessibility for consumers to purchase products from smaller vendors.

## Recognizable marketplace

To improve the ease of use, Locl's interface is carefully crafted to emulate other popular marketplace applications such as Facebook Marketplace and Craigslist. Because shoppers will already be accustomed to our app, it'll far improve the overall user experience.

# How we built it

Locl revolves around a web app interface to allow shoppers and vendors to buy and sell produce.

## Flask

The crux of Locl centers on our Flask server. From there, we use requests and render\_templates() to populate our website with GET and POST requests.

## Supabase

We use Supabase and PostgreSQL to store our product, market, virtual credit card, and user information. Because Flask is a Python library, we use Supabase's community managed Python library to insert and update data.

## Checkbook.io

We use Checkbook.io's Payfac API to create transactions between shoppers and vendors. When people create an account on Locl, they are automatically added as a user in Checkbook with the `POST /v3/user` endpoint. Meanwhile, to onboard both local farmers and shoppers painlessly, we offer a bankless solution with Checkbook’s virtual credit card using the `POST /v3/account/vcc` endpoint.

First, shoppers deposit credits into their Locl account from the EBT card. The EBT funds are later redeemed with the state government by Locl. Whenever a user buys an item, we use the `POST /v3/check/digital` endpoint to create a transaction between them and the stores to pay for the goods. From there, vendors can also spend their funds as if it were a prepaid debit card. By using Checkbook’s API, we’re able to break down the financial barrier of having a bank account for low-income shoppers to buy fresh produce from local suppliers, when they otherwise wouldn’t have been able to.

# Challenges we encountered

Because we were all new to using these APIs, we were initially unclear about what actions they could support. For example, we wanted to use You.com API to build our marketplace. However, it soon became apparent that we couldn't embed their API into our static HTML page as we'd assume. Thus, we had to pivot to creating our own cards with Jinja.

# Looking forward

In the future, we hope to advance our API services to provide a wider breadth of services which would include more than just produce from local farmers markets. Given a longer timeframe, a few features we'd like to implement include:

* a search and filtering system to show shoppers their preferred goods.
* an automated redemption system with the state government for EBT.
* improved security and encryption for all API calls and database queries.

# Ethics

SNAP (Supplemental Nutrition Assistance Program), otherwise known as food stamps, is a government program that aids low-income families and individuals to purchase food. The inaccessibility of healthy foods is a pressing problem because there is a small number of grocery stores that accept food stamps, which are often limited to large, chain grocery stores that are not always accessible. Beyond this, these grocery stores often lack healthy food options in favor of highly-processed goods.

When doing further research into this issue, we were fortunate to have a team member who has knowledge about SNAP benefits through firsthand experience in classroom settings and at food banks. Through this, we learned about EBT (Electronic Benefit Transfer) cards, as well as their limitations. The only stores that can support EBT payments must offer a selection for each of the staple food categories, which prevents local markets and farmers from accepting food stamps as payment.

To tackle this issue of the limited accessibility of healthy foods for SNAP benefit users, we came up with Locl, an online platform that allows local markets and farmers to list fresh produce for EBT cardholders to purchase with food stamps. When creating Locl, we adhered to our goal of connecting food stamp users with healthy, ethically sourced foods in a sustainable manner. However, there are still many ethical challenges that must be explored further.

First, to use Locl, users would require a portable electronic device and an internet connection due to it being an online platform. The Pew Research center states that 29% of adults with incomes below $30,000/year do not have access to a smartphone and 44% do not have portable internet access. This would greatly lessen the range of individuals that we aim to serve.

Second, though Locl aims to serve SNAP beneficiaries, we also hope to aid local markets and farmers by increasing the number of potential customers. However, Locl runs the risk of displaying certain produce items or marketplaces disproportionately in comparison to others, which could create imbalances and inequities between all stakeholders involved. Furthermore, this display imbalance could limit user knowledge about certain marketplaces.

Third, Locl aims to increase ethical consumerism by connecting its users with sustainable markets and farmers. However, there arises the issue of selecting which markets and farmers to support on our platform. While considering baselines that we would expect marketplaces to meet to be displayed on Locl, we recognized that sustainability can be measured through a wide number of factors- labor, resources used, pollution levels, and began wondering whether we prioritize sustainability of items we market or the health of users. One example of this is meat, a popular food product which is known for its high health benefits, but similarly high water consumption and greenhouse gas levels. Narrowing these down could greatly limit the display of certain products.

Fourth, Locl does not have an option for users to filter the results that are displayed to them. Many EBT cardholders say that they do not use their benefits to make online purchases due to the difficulty of finding items on online store pages that qualify for their benefits as well as their dietary needs. Thus, our lack of a filter option would cause certain users to have increased difficulty in finding food options for themselves.

Our next step for Locl is to address the ethical concerns above, as well as explore ways to make it more accessible and well-known. However, there are still many components to consider from a sociotechnical lens. Currently, only 4% of SNAP beneficiaries make online purchases with their EBT cards. This small percentage may stem from reasons that range from lack of internet access, to not being aware that online options are available. We hope that with Locl, food stamp users will have increased access to healthy food options and local markets and farmers will have an increased customer-base.

# References

<https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2019.305325>
<https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-6546-2>
<https://www.ibisworld.com/industry-statistics/number-of-businesses/supermarkets-grocery-stores-united-states/> <https://www.masslive.com/food/2022/01/these-are-the-top-10-unhealthiest-grocery-items-you-can-buy-in-the-united-states-according-to-moneywise.html>
<https://farmersmarketcoalition.org/education/qanda/>
<https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-6546-2>
<https://news.climate.columbia.edu/2019/08/09/farmers-market-week-2019/>
## FlyBabyFly

[link](https://i.imgur.com/9uIFF1K.png)
[link](https://i.imgur.com/O55PRJE.jpg)

## What it does

Its a mobile app that matches people with travel destinations while providing great deals.

Swipe right to like! Swipe left to see next deal.

After you like a city you can learn about the city's attractions, landmarks and history. It can help you book right from the app, just pick a date!

## How we built it

We used React Native and JetBlue's provided data.

## Accomplishments that we're proud of

We believe this is a refreshing approach to help people discover new cities and help them plan their next vacation

## What's next for FlyBabyFly

* Add vacation packages that include hotel bundles for better prices.