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Computerised prediction of healing for venous leg ulcers
Quoc Cuong Ngo1, Rajna Ogrin2,3, Dinesh Kant Kumar4
1School of Engineering, STEM College, RMIT University, 124 Latrobe Street, Melbourne, VIC, 3000, Australia.
Scientific Reports
|October 26, 2022
Summary
Early prediction of delayed healing for venous leg ulcers is possible using thermal imaging. This non-contact machine learning approach can identify non-healing wounds at week 0, improving patient management.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Machine Learning
Background:
- Venous leg ulcers (VLUs) often exhibit delayed healing, necessitating early intervention strategies.
- Current methods for predicting VLU healing are often delayed and require physical contact.
Purpose of the Study:
- To develop a computerised, non-contact framework for early prediction of VLU healing using thermal imaging.
- To assess the feasibility of predicting VLU healing at week 0 in home settings.
Main Methods:
- Collected thermal images of 56 participants' VLUs at week 0 in their homes.
- Extracted textural features from week 0 thermal images.
- Utilized a Bayesian neural network with principal components of textural features for classification.
Main Results:
- Unhealed VLUs showed higher grey tone variation in thermal images.
- The Bayesian neural network achieved 78.57% sensitivity and 60.00% specificity in discriminating healed from unhealed wounds.
- This method enables prediction at week 0, significantly earlier than current contact-based methods.
Conclusions:
- Thermal imaging combined with machine learning offers a promising non-contact method for early prediction of VLU healing.
- This approach can facilitate timely initiation of adjuvant therapies for improved patient outcomes.
- The framework is suitable for home-based assessments, enhancing patient convenience and accessibility.
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