I wanted to build a solution to check blood sugar levels non-invasively.
What it does
We wanted to investigate if it is possible to predict blood glucose levels in realtime via retinal scans.
Our work is motivated by this paper, where the researchers describe constriction of vascular smooth muscle due to hyperglycemia - https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1111/bph.13399
Since the retina is rich in vascular smooth muscle, we would like to study the effects of constriction and expansion of such tissue and determine if it is correlated to blood glucose levels
How we built it
We captured fundus photographs using a D-eye digital ophthalmoscope. The retinal images were segmented using SIFT detectors . We then built a image super-resolution model based on SR-GAN . code is located on Github
Out plan was to collect realtime blood glucose readings using a Dexcom CGM and use that to build a prediction model to infer glucose readings from retinal imagery based on Attention models, but we ran out of time
Challenges we ran into
- not enough time to collect training data to finish the project
we were able to perform super-resolution of the raw retina imagery . but not enough time and data to test predictions
What's next for Noninvasive BloodGlucose prediction via fundus imagery
collect real time data to test theory