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Rourkela, India – A research team at the National Institute of Technology (NIT) Rourkela, led by Mirza Khalid Baig, Assistant Professor in the Department of Biotechnology and Medical Engineering, has developed a cutting-edge artificial intelligence (AI) model aimed at enhancing blood sugar predictions for people with diabetes.
Co-authored by Baig and his research scholar, Deepjyoti Kalita, the findings of this groundbreaking study have been published in the IEEE Journal of Biomedical and Health Informatics. Their work introduces a machine-learning model that significantly improves the accuracy of blood glucose level forecasting, empowering individuals and healthcare providers to make better-informed, personalized treatment decisions.
A Growing Health Challenge
Diabetes remains a pressing health concern in India, with projections indicating that the number of cases will rise to 124.9 million by 2045. Effective diabetes management requires consistent blood glucose monitoring to prevent dangerous spikes (hyperglycemia) and drops (hypoglycemia). However, challenges such as a shortage of diabetes specialists, unequal access to healthcare, poor medication adherence, and inadequate self-care often hinder patients from maintaining stable blood sugar levels. These difficulties increase the risk of severe health complications.
The Role of AI in Diabetes Management
Emerging digital health technologies, particularly AI-driven tools, are proving to be instrumental in revolutionizing diabetes care and reducing treatment costs. Machine learning (ML) has been widely applied in diabetes research, aiding in everything from fundamental studies to the development of predictive tools that assist doctors and patients in making timely medical decisions. However, existing AI predictive models often operate as a “black box,” making their decision-making processes difficult to interpret. This lack of transparency can lead to hesitation in trusting their accuracy. Furthermore, conventional forecasting methods, including statistical models and basic neural networks, frequently fail to capture long-term glucose fluctuations, requiring extensive manual fine-tuning.
A Smarter AI Model
The NIT Rourkela research team has tackled these issues by designing a deep learning model that leverages past blood sugar trends to predict future glucose levels with greater accuracy than current methods. Unlike traditional forecasting techniques that struggle with long-term trends and demand manual adjustments, this AI-driven system autonomously processes glucose data, identifies crucial patterns, and generates precise predictions.
Speaking about the innovative approach, Prof. Mirza Khalid Baig stated, “According to the results of the ICMR-INDIAB study released in 2023, the overall prevalence of diabetes in our country is 11.4 percent, while prediabetes stands at 15.3 percent. This underscores the urgent need for new solutions to address the problem. Our model employs multi-head attention layers within a neural basis expansion network, enabling it to focus on the most relevant data points while filtering out unnecessary noise. This results in superior performance without requiring vast amounts of training data or excessive computational resources.”
By optimizing both precision and efficiency, the AI model has the potential to integrate seamlessly into digital health platforms, aiding both doctors and patients in better managing diabetes.
Enhancing Accuracy and Accessibility
The AI model developed by NIT Rourkela outperforms existing forecasting methods by delivering more reliable blood sugar predictions. By prioritizing key trends in an individual’s glucose levels, it enables highly personalized forecasts, leading to timely adjustments in insulin doses, dietary choices, and physical activity. Furthermore, the model has been designed for compatibility with everyday devices such as smartphones and insulin pumps, ensuring its accessibility for widespread use.
Future Applications and Clinical Trials
Looking ahead, this AI-driven innovation has the potential to enhance diabetes care across multiple applications. It could be integrated into smart insulin pumps to automate insulin delivery, incorporated into mobile health applications for real-time glucose tracking, or deployed in clinical settings to assist doctors in crafting individualized treatment plans.
The research team is now focused on testing the model through extensive clinical trials at hospitals, collaborating with leading diabetologists in Odisha, including Dr. Jayanta Kumar Panda and his team. Additionally, they acknowledge the support received from the Department of Science and Technology (DST), the Department of Biotechnology (DBT), and NIT Rourkela for this research initiative.
Disclaimer:
The information presented in this article is based on research findings and should not be used as a substitute for medical advice. Patients should consult healthcare professionals before making any changes to their diabetes management plans.
