This GitHub organization was created to showcase Team 11's project in Track 3 (machine learning track) of the MIT FutureMakers Summer 2023 Create-a-thon hosted by SureStart × MIT RAISE! Our team is comprised of high school and undergraduate students from across the U.S. with a desire to make a strong impact in the world of AI/ML.
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| Tameem | Marco | Emily |
| (Mentee) | (Mentee) | (Mentee) |
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| Thomas | Jiwoo | Deahan |
| (Mentee) | (Mentee) | (Mentor) |
VisionGuardian is a combination of machine learning models designed to help you assess your eye health conveniently and to provide detailed insights for preventative care. Through the power of AI/ML technology, a user can simply take a picture of their eyes and film themselves doing a quick screening test to detect fatal eye diseases such as glaucoma and cataracts. This enables them to get personalized insights, promote early detection, and prioritize your eye health. See more detailed information in our organization's other repositories!
Note
Currently, this isn't a fully functional mobile application. We've only implemented bare bones glaucoma + cataract detection scripts in Python.
Since our chosen theme for this year's Create-a-thon was Physical Health & Mental Well-Being Management, we intended to keep the United Nations' Sustainable Development Goal #3 (Good Health & Wellbeing) in mind while creating our project. We were able to make it to the final round of presentations, and at the end of the competition, our team proudly came in second place out of a total of seven teams! We encourage anyone who would like to see our presentation to watch the full recording on YouTube!
- Figma was utilized to create our app prototype.
- A wide variety of Python packages were used to create our detection systems.
- Cataract detection:
- TensorFlow to train the convolutional neural network.
- Glaucoma detection:
- OpenCV to leveraging computer vision.
- MediaPipe for pose detection.
- Cataract detection:
We plan to continue adding more robust features and developing more sophisticated methods to improve on what we currently have and to detect other potential eye problems using AI/ML.
- For example, instead of only utilizing a binary classifier for cataract detection, we may instead create a regression model to detect what stage of cataracts a user may be experiencing.
- Some other things that could potentially be detectable include: detecting diabetic retinopathy, macular degeneration, retinal detachment, optic neuropathies, uveitis, keratoconus, retinitis pigmentosa, conjunctivitis, etc.
- Additionally, we're also planning to collect more data points from our users to provide more accurate results. This may include their age, height, weight, gender, physical activity, sleeping patterns, diet, etc.
