IIT Jodhpur Researchers Develop Smartphone-based Glucose Testing System

By integrating this tech with smartphones, researchers claim to have streamlined glucose level tracking, making it faster and more personalized.

Researchers at the Indian Institute of Technology (IIT) Jodhpur have developed a system that utilizes smartphones to test glucose levels in patients, providing quick and accessible results. 

This innovative approach connects a paper-based analytical device (PAD) to any smartphone using an Android app, enabling the detection of glucose levels within a concentration range of 10−40 mM.

PADs, portable devices that have transformed point-of-need testing, play a central role in this system. Equipped with lab-based functionalized biodegradable paper, these devices alter their hue based on the glucose level present in the sample. 

By integrating this technology with smartphones, researchers claim to have streamlined the process of tracking glucose levels, making it faster and more personalized.

How Does it Work?

Designed for personal use, this device offers on-the-spot glucose testing without the need for technical laboratory settings. 

The current cost per unit is estimated at only INR 10 in the lab. The research team aims to further reduce production costs to INR 5 during mass production, making it accessible to a wider population.

Sharing thoughts, Dr Ankur Gupta, associate professor, Department of Mechanical Engineering, IIT Jodhpur and one of the study's authors, said, "Smartphones offer seamless integration with other technologies and platforms. The ability to connect the smartphone-based spot detection framework to a larger network or database can facilitate remote monitoring, data storage, and sharing of results. This connectivity can be crucial for healthcare professionals or researchers."

One major challenge with PADs is their dependence on specific light conditions to function effectively. 

However, the system developed by the IIT Jodhpur researchers promises to eliminate this limitation, enabling PADs to operate and transmit information to smartphones under various light conditions.

Machine Learning Integration

To ensure reliability and accuracy, artificial glucose samples were used to develop the smartphone app. 

Machine learning techniques were employed to process images of colored samples, ensuring that the intensity of color from the PAD remained consistent across different light conditions and smartphone cameras. 

This approach ensures compatibility with smartphones featuring varying camera optics.

"This study demonstrates that this developed system is equipped for initial disease screening at the user end. By incorporating machine learning techniques, the platform can provide reliable and accurate results, thus paving the way for estimating the accuracy of the results for improved initial healthcare screening and diagnosis of any disease," Dr Gupta noted.

While the current framework focuses on glucose testing, the researchers envision its adaptation for detecting other diseases. The team is exploring the simultaneous detection of glucose, uric acid, and lactate using different color indicators as unique codes. 

This modular approach can be tailored for various target analytes, enzymes, and indicators, paving the way for screening and diagnostic analysis of a wide range of diseases.

By leveraging the ubiquitous nature of smartphones and innovative paper-based analytical devices, this system promises to offer a cost-effective, accessible, and environmentally friendly solution for glucose testing. 

With its potential for adaptation to detect other diseases, it promises to revolutionize healthcare screening and diagnosis, bringing personalized healthcare solutions within reach for millions.

In another development, researchers at the IIT Jodhpur developed a human breath sensor that claims to be the first of its kind. It is a "Made in India" product based on metal oxides and nanosilicon.

The primary purpose of this device is to measure alcohol content in the breath, particularly in cases of drunk driving. 

However, with adjustments in sensing layers and incorporating an array of sensors (resembling an electronic nose), the sensor can also prove invaluable for characterizing various diseases.