Pressure ulcers are a persistent issue in bedridden patients, especially in intensive care, rehabilitation, and long-term care, leading to pain, infection, and extended hospital stays. Current risk assessments rely on intermittent scoring and clinical judgment, failing to account for continuous changes in body posture, tissue loading, and mechanical tolerance. This conceptual framework proposes a physics-informed graph neural network to predict pressure ulcer risk by integrating data from body position sensors, local tissue loading, and skin perfusion measurements into a dynamic, personalized model. The model represents the body as a graph, with nodes representing pressure-prone areas and edges indicating anatomical and mechanical connections. Tissue stress, perfusion data, and posture features are processed through network layers constrained by soft-tissue mechanics. By encoding the relationship between external forces, internal tissue deformation, ischemia, and damage, the framework allows risk propagation across adjacent anatomical regions. This approach offers a path for continuous, personalized pressure ulcer risk monitoring, laying the foundation for clinical validation and sensor integration.
Clinical Intelligence Research Press