Abstract
BackgroundNumerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring.MethodsThis research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site.ResultsFor (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value.ConclusionsThis augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed.Trial registration201505164RIND, 201803108RSB.
Highlights
Numerous patients suffer from chronic wounds and wound infections nowadays
With the growing demand for more efficient wound care after surgery, the development of information technology to assist the work of medical personnel has become a major trend to address these types of problems and reduce the costs of chronic wound care
We propose an algorithm to position wound suture site using feature points extracted by morphological cross-shaped features from the wound area
Summary
Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. The current methods employed to solve this type of problem include: Dini et al [2] use infrared photography to interpret wound temperature changes; Lubeley et al [3] propose mobile three-dimensional (3D) wound measurement; Hani et al [4] perform 3D surface scans of woundcs to obtain wound top area, true surface area, depth, and volume; Wannous et al [5] develop imaging methods with depth of field information to judge the depths of wounds These methods are expensive and require special photographic equipment; they cannot be widely used on general surgery patients
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