Diabetes is the fastest growing chronic disease affecting over 400 million people worldwide. The Diabetes Prevalence Survey 2017 of Pakistan (see Figure 1) has revealed staggering 16.98 percent prevalence of diabetes as 35.3 million people among the adult population are found diabetic in Pakistan. A
Iris based Noninvasive Glucose Monitoring System
Diabetes is the fastest growing chronic disease affecting over 400 million people worldwide. The Diabetes Prevalence Survey 2017 of Pakistan (see Figure 1) has revealed staggering 16.98 percent prevalence of diabetes as 35.3 million people among the adult population are found diabetic in Pakistan. According to recent studies of WHO, right now, 1 person out of 11 adults have diabetes in the world. Pakistan is ranked 7th in the world for diabetes prevalence. The patients have to constantly monitor and measure their blood sugar levels many times in a day and regulate their diet accordingly. The conventional techniques for sugar testing use invasive finger stick tests which are not only painful but are also inefficient and costly i.e., a new test strip is required every single time.
To overcome these problems, in this project, we aim to develop a non-invasive, painless and cost-effective method for blood glucose monitoring using images of the human eyes. The glucose levels in the human eye are correlated with the glucose levels in blood. Thus, changes in the blood glucose level lead to changes in the glucose concentration in the fluid of the eye. This leads to very subtle differences in how the iris appears. In this work, we develop a complete system with a mobile application interface, which requires the patient to capture a high definition image of their eye with their smart phone using a macro lens. The captured image will be the input of our Iris based non-invasive glucose monitoring mobile App. The mobile app will communicate with the server to process images and retrieve results.The server will detect the subtle differences in the captured iris image using a pre-trained deep learning based model by detecting the changes in the fluid of the eye. The mobile app will report the sugar level of a person by processing the changes in Iris shape.
The broader objectives of our project are the following:
The detailed steps involved in the proposed Glucose Monitoring System are shown in the following diagram:
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Macro Lens | Equipment | 1 | 15000 | 15000 |
| GPU (NVIDIA K40) | Equipment | 1 | 55000 | 55000 |
| Total in (Rs) | 70000 |
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