Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm’s burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.
Multispectral imaging (MSI) was implemented to develop a burn diagnostic device that will assist burn surgeons in planning and performing burn debridement surgery by classifying burn tissue. In order to build a burn classification model, training data that accurately represents the burn tissue is needed. Acquiring accurate training data is difficult, in part because the labeling of raw MSI data to the appropriate tissue classes is prone to errors. We hypothesized that these difficulties could be surmounted by removing outliers from the training dataset, leading to an improvement in the classification accuracy. A swine burn model was developed to build an initial MSI training database and study an algorithm’s ability to classify clinically important tissues present in a burn injury. Once the ground-truth database was generated from the swine images, we then developed a multi-stage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm’s accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data from wavelength space, and test accuracy was improved from 63% to 76%. Establishing this simple method of conditioning for the training data improved the accuracy of the algorithm to match the current standard of care in burn injury assessment. Given that there are few burn surgeons and burn care facilities in the United States, this technology is expected to improve the standard of burn care for burn patients with less access to specialized facilities.
We present a non-contact, reflective photoplethysmogram (PPG) imaging method and a prototype system for identifying
the presence of dermal burn wounds during a burn debridement surgery. This system aims to provide assistance to
clinicians and surgeons in the process of dermal wound management and wound triage decisions. We examined the
system variables of illumination uniformity and intensity and present our findings. An LED array, a tungsten light
source, and eventually high-power LED emitters were studied as illumination methods for our PPG imaging device.
These three different illumination sources were tested in a controlled tissue phantom model and an animal burn model.
We found that the low heat and even illumination pattern using high power LED emitters provided a substantial
improvement to the collected PPG signal in our animal burn model. These improvements allow the PPG signal from
different pixels to be comparable in both time-domain and frequency-domain, simplify the illumination subsystem
complexity, and remove the necessity of using high dynamic range cameras. Through the burn model output comparison,
such as the blood volume in animal burn data and controlled tissue phantom model, our optical improvements have led
to more clinically applicable images to aid in burn assessment.
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