Abstract
Background: The surgical wound is a unique problem requiring continuous postoperative care, and mobile health technology is implemented to bridge the care gap. Our study aim was to design an integrated framework to support the diagnosis of wound infection. Methods: We used a computer-vision approach based on supervised learning techniques and machine learning algorithms, to help detect the wound region of interest (ROI) and classify wound infection features. The intersection-union test (IUT) was used to evaluate the accuracy of the detection of color card and wound ROI. The area under the receiver operating characteristic curve (AUC) of our model was adopted in comparison with different machine learning approaches. Results: 480 wound photographs were taken from 100 patients for analysis. The average value of IUT on the validation set with fivefold stratification to detect wound ROI was 0.775. For prediction of wound infection, our model achieved a significantly higher AUC score (83.3%) than the other three methods (kernel support vector machines, 44.4%; random forest, 67.1%; gradient boosting classifier, 66.9%). Conclusions: Our evaluation of a prospectively collected wound database demonstrates the effectiveness and reliability of the proposed system, which has been developed for automatic detection of wound infections in patients undergoing surgical procedures.
Highlights
Surgical site infection (SSI) is one of the most common surgical infectious complications and can increase the length of hospital stay, costs, the number of readmissions, and the need for further wound debridement [1,2]
Our algorithm achieved an accuracy of 79.5%, recall of 77.1%, precision of 82.7%, F1 score of 79.4%, and areaand under the receiver operating curve (AUC) score of 83.3%, whereas the other three methods achieved lower AUC scores
We introduced a unified framework for automatic analysis of wound condition with the use of supervised machine learning, to overcome the shortage of data on wound images
Summary
Surgical site infection (SSI) is one of the most common surgical infectious complications and can increase the length of hospital stay, costs, the number of readmissions, and the need for further wound debridement [1,2]. If the early signs of SSI are not detected by patients or caregivers, delays in visits to the clinic can result in severe wound complications that require hospital readmission, with or without surgical intervention [5]. For prediction of wound infection, our model achieved a significantly higher AUC score (83.3%) than the other three methods (kernel support vector machines, 44.4%; random forest, 67.1%; gradient boosting classifier, 66.9%). Conclusions: Our evaluation of a prospectively collected wound database demonstrates the effectiveness and reliability of the proposed system, which has been developed for automatic detection of wound infections in patients undergoing surgical procedures
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