Thesis
Development of an in-dressing wound infection sensor
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2022
- Thesis identifier
- T16265
- Person Identifier (Local)
- 201682579
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Infection can be detrimental to the wound healing process, prolonging healing times andpotentially leading to severe consequences. Current gold-standard methods of infectiondetection rely upon lengthy laboratory-based culturing procedures, typically taking at least 48hours. Rapid detection of wound infection and determination of the causative pathogens arevital to ensuring timely, targeted treatment. In this study, the development of an in-dressingelectrochemical sensor that can detect wound infection in real time was advanced. Different screen-printed sensor materials were explored with the aim of determining the most suitable for rapid bacterial detection. The Ag/AgCl sensors studied were unsuitable: both bactericidal properties and instability during measurement were observed. Conversely, for both the carbon and platinised carbon electrodes (PCE) studied, wound pathogens were detected within 30 minutes in culture medium. Normalised impedance signatures characteristic of bacterial growth were uncovered. Due to their superior response, it was concluded that the PCE sensors were most appropriate for this application. Artificial wound bed models were created, further evidencing the suitability of the PCE sensor in particular for bacterial detection in the wound environment. These models incorporated factors such as a simulated wound fluid, a collagen gel matrix and a range of wound dressings. Finally, different algorithmic approaches were used to analyse the large volume of impedance data obtained. A rate of change based approach was developed to support bacterial concentration estimation. Further, artificial neural networks enabled bacterial detection and species identification. To advance this device, future research should include conducting clinical studies to test the sensors with real wound samples and in a real wound environment. This would reveal their suitability for adoption into a future medical device. Additionally, using this clinical data and added laboratory data the bacterial detection and identification algorithms could be advanced.
- Advisor / supervisor
- Connolly, Patricia
- Resource Type
- Note
- This thesis was previously held under moratorium from 30th May 2022 until 30th May 2024.
- DOI
- Funder
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PDF of thesis T16265 | 2022-06-06 | Öffentlich | Herunterladen |