Authors:

Neha Saleha

Date Created:

Not specified

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About Paper:

Chronic wounds, including diabetic ulcers and pressure injuries, affect over 6.5 million individuals in the United States, leading to morbidity, infection risk, and substantial healthcare costs. Current wound assessment relies on subjective visual inspections and intermittent sampling, often delaying critical complication detection. Wound exudate contains invaluable biochemical information that, if monitored continuously and spatially, could provide early, objective indicators of wound status and guide timely interventions. This project introduces EXHALE (Exudate and Hydration Analysis for Lesion Evolution), a conformable intelligent wound dressing designed for real-time, spatially resolved monitoring of wound exudate. Our innovation integrates multimodal sensors within a biocompatible hydrogel platform to assess both the diffusion dynamics and critical biochemical composition, including pH, protein concentration, and electrolytes. This continuous, objective data stream promises unprecedented insight into wound microenvironments. We will evaluate EXHALE's performance and its ability to capture dynamic fluid profiles using advanced wound phantom models, validating the platform's responsiveness and accuracy. Sensor data will feed sophisticated machine learning algorithms, allowing wound status classification and early prediction of complications like infection or stagnation. Our methodology involves developing artificial wound exudates by controlling pH, glucose, viscosity and temperature. Initial sensor testing on these mimics is expected to show transudates with physiological pH (~7.3-7.5) and normal glucose (~8.3 mmol/L). Chronic/infected exudates will exhibit distinctly alkaline pH (~7.8-8.9) and significantly lower glucose (<0.5 mmol/L),. EXHALE represents a shift in wound management. Its successful development will yield a non-invasive, intelligent tool capable of delivering actionable insights directly to clinicians at a low cost. † Presenting Undergrad Author; ‡ Contributing Undergrad Author; * Undergrad Acknowledgment Keywords: Chronic Wounds; Wound Exudate; Multimodal Sensing; Real-Time Monitoring; Machine Learning

Source:

Purdue University / 2025

Topics:

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Co-authors:

Neha Saleha