Visionary Glucometer
Techiee Hackers
Sanjai L
Naveen Kumar S
Mithreshwar MSA
Subash E
01
02
03
Team Members
04
Table of contents
Problem
01
Solution
02
Product
03
Evidence
04
Problem
400 Million
Diabetes is a chronic medical condition characterized by elevated blood sugar levels, which can lead to various health complications if not properly managed.
5%
90-95%
of people have diabetes across the world
of people are affected by type - 2 diabetes
of people affected by type - 1 diabetes
Problem
Regularly a Diabetes patient want to monitor or check their Glucose level nearly 10 times per day.
The problem here is that the invasive finger stick test is not only painful, inefficient and costly because we need a new Strip every single time that is extremely expensive.
Problem
Solution
Introducing our innovative solution to the challenges posed by invasive glucose prediction methods - the Visionary Glucometer.
This cutting-edge project aims to transform the landscape of diabetes management by offering a non-invasive approach to glucose monitoring through iris image analysis using a cutting- edge deep learning model.
Solution
Evidence
Concentration of aqueuos humour
Intra Ocular Pressure(IOP)
The aqueous humour is a cleary watery fluid fills ths the anterior chamber of the eye.
Evidence
This graph shows that the levels of glucose
found in the eye correlate very well with
those in the blood in our veins
i.e. Glucose Levels in the blood stream is
proportional to the concentration of
glucose in aqueous humor.
The concentration of glucose in aqueous humour impacts the pressure in the eye called Intra Ocular Pressure(IOP).
This Pressure is responsible for the deformation of the Iris(Crypts, Ridges and Furrows).
Evidence
Deep Learning-CNN
Loss: 22.3321
Validation_Loss: 158.6744
R-squared Score: 0.945
Utilizing CNN architecture to analyze iris image structure unveils hidden patterns for non-invasive glucose prediction.
CNN-based analysis of iris images deciphers intricate features, revealing correlations crucial for glucose level estimation.
CNNs decode iris image data, unlocking valuable insights into glucose dynamics through structural analysis.
Model Evaluation
Product
By using cheap lens adopt on mobile camera by holding up to their face which able to capture the high quality images of an eye.
Our Mobile Applications analyze the iris structure of the captured image and turns into glucose reading within 10 seconds at no cost and also reduce the risk of traditional blood glucose level monitoring method.
Product
It predicts the glucose by capturing eye image
Glucose Prediction
Product
Food Recommendation
Nutri Gluco
It provides personalized food recommendation based on your glucose level
It suggest recipes based on ingredients in your fridge
Impact of solution
Easier Monitoring for Children and Elderly
Painless Monitoring
Reduced Risk of Infection
User-Friendly and Convenient
Continuous monitoring
Economical
Future Plan
It recommends the
exercise
Glucose Prediction
Future Work
Easy Communication
Remote monitoring
Allows to communicate with doctors
keep track of patient
health
Thank you
Techiee Hackers