Hybrid Modeling of the Insulin-Glucose System: Combining Linear and Data-Driven Models for Artificial Pancreas

Type 1 diabetes mellitus is a chronic condition that requires insulin delivery to maintain blood glucose levels within a desired range. The artificial pancreas (AP), which integrates a continuous glucose sensor, an insulin pump, and a control algorithm, is a promising solution for automating insulin delivery. Designing optimal controllers for the AP is crucial to its effectiveness. Existing approaches often rely on advanced controllers based on models of the insulin-glucose system. However, t...

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling

Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden by automating insulin delivery through advanced control algorithms. However, the effectiveness of these systems depends on accurate modeling of glucose-insulin dynamics, which traditional mathematical models often fail to capture due to their inability to adapt to patient-specific variations. This study introduces a Biological-Informed Recurrent N...

«Herone Alessandrino de gli Spiritali tradotto nella nostra lingua»: Bernardo Buontalenti, Oreste Vannocci Biringucci, Giambattista Aleotti

Integrating Uncertainty Into U-Net Robustness Evaluation Under Natural MRI Alterations: Application to Kidney Segmentation

The adoption of deep learning (DL) for medical image segmentation in clinical practice is limited by interpretability issues and sensitivity to out-of-distribution data. Robust models that maintain stable accuracy while offering reliable uncertainty estimates in the presence of data variations should be required. We propose a framework to evaluate the robustness of U-Net-based segmentation on a kidney MRI dataset, simulating common abdominal MRI distortions. Robustness is assessed using a nov...