AMD Monitor

Inspiration

We were inspired to develop an app for detecting age-related macular degeneration because one of our teammate's grandmother has the condition and we wanted to find a way to help her and others who have it. We believe that technology can be used to improve people's lives, and we wanted to use our skills as data scientists and software developers to create something that could make a difference.

What it does

The app we are developing uses a combination of techniques to detect age-related macular degeneration. First, it asks the user to mark any places on a grid that appear distorted or blurry. This allows the app to gather information about the user's visual acuity and detect potential signs of the condition. Next, the app presents the user with a series of images and asks them to select specific ones. This allows the app to gather additional information about the user's visual abilities and helps the algorithm to identify patterns that may indicate the presence of macular degeneration. As the user performs these tasks, the app tracks their vision, the distance between their eyes and the smartphone screen, and where they are looking on the screen. This information is used to provide more accurate results and improve the performance of the algorithm. By performing these tasks periodically, the app can help to analyze the user's vision over time and potentially detect early signs of age-related macular degeneration. This can allow people with the condition to get an early diagnosis and receive treatment sooner, which can help to preserve their vision.

How we built it

In order to create our app, we used the tools Xcode, Swift and ARKit to design it from scratch. However, our previous research on eye diseases aided us in completing the minimum viable product (MVP) of the app.

Challenges we ran into

There are several challenges we ran into while building this technology. First, this was our first time attempting to deploy machine learning models to an iOS application. Next, accurate eye-tracking with high accuracy was not as easy as we were taught. We combined ARKit with custom solutions to achieve accurate results.

Accomplishments that we are proud of.

Some of the accomplishments that we are proud of include achieving a high level of accuracy during testing on friends and family, receiving positive feedback from an optometrist who expressed interest in collaborating with us, and successfully building a fully functional app that meets most of our original goals.

What is next for AMD monitor

After fixing any bugs that we encounter, the next step is to present our app to ophthalmologists and optometrists in order to begin clinical tests. This will help us to ensure that our app is effective and reliable in detecting and diagnosing eye diseases.