Invention Description
Type 1 diabetes (T1D) affects nearly 10 million people worldwide and occurs when the immune system destroys the pancreas's insulin-producing beta cells. Managing T1D is notoriously difficult, requiring constant glucose monitoring and precise insulin dosing. Even with advanced tools like continuous glucose monitors (CGMs) and automated insulin delivery (AID) systems, patients still struggle with dysglycemia and would benefit significantly from a deeper understanding of how to fine-tune their insulin doses for optimal blood sugar control.
Researchers at Arizona State University have developed an innovative clinical decision support system which integrates sensors, processors, data communication, and machine learning algorithms to assist clinicians in preventing hyperglycemia in T1D patients. By analyzing continuous glucose monitor and insulin pump data, it offers advanced analytics and simulations to suggest optimal insulin timing and dosage adjustments. This platform features predictive models which are all accessible via an intuitive portal for real-time data visualization and decision-making.
This technology is an AI-powered clinical decision support system designed to optimize diabetes management through actionable insights and advanced glucose control recommendations.
Potential Applications
- Companies developing diabetes management software and medical devices
- Hospitals and clinics managing type 1 diabetes patients
- Endocrinologists and diabetes care specialists
- Diabetes research centers and academic institutions
- Integrated care platforms seeking AI-driven decision support tools
Benefits and Advantages
- Provides actionable recommendations rather than just visualizing data
- Utilizes AI-driven predictive models for accurate hyperglycemia forecasting
- Enables clinicians to simulate and evaluate the effects of insulin and carbohydrate adjustments
- Integrates continuous glucose and insulin data for personalized treatment plans
- User-friendly portal streamlines clinical workflows and decision-making