Sequential Change of Wound Calculated by Image Analysis Using a Color Patch Method during a Secondary Intention Healing

Abstract

BackgroundPhotographs of skin wounds have the most important information during the secondary intention healing (SIH). However, there is no standard method for handling those images and analyzing them efficiently and conveniently.ObjectiveTo investigate the sequential changes of SIH depending on the body sites using a color patch methodMethodsWe performed retrospective reviews of 30 patients (11 facial and 19 non-facial areas) who underwent SIH for the restoration of skin defects and captured sequential photographs with a color patch which is specially designed for automatically calculating defect and scar sizes.ResultsUsing a novel image analysis method with a color patch, skin defects were calculated more accurately (range of error rate: -3.39% ~ + 3.05%). All patients had smaller scar size than the original defect size after SIH treatment (rates of decrease: 18.8% ~ 86.1%), and facial area showed significantly higher decrease rate compared with the non-facial area such as scalp and extremities (67.05 ± 12.48 vs. 53.29 ± 18.11, P < 0.05). From the result of estimating the date corresponding to the half of the final decrement, all of the facial area showed improvements within two weeks (8.45 ± 3.91), and non-facial area needed 14.33 ± 9.78 days.ConclusionFrom the results of sequential changes of skin defects, SIH can be recommended as an alternative treatment method for restoration with more careful dressing for initial two weeks.

Chitosan-polyvinyl alcohol nanoscale liquid film-forming system facilitates MRSA-infected wound healing by enhancing antibacterial and antibiofilm properties

“…In 2012, 7.5% of American patients who underwent surgical resection for cancer suffered from wounds. 17 Wound healing refers to a complex cascade of cellular events involving four intricate and overlapping phases: hemostasis, inflammation, proliferation, and tissue remodeling. 18 Many factors influence wound healing, such as the location of the wound and concomitant diseases.…”

“…First, wound dressing materials and advanced products have been developed for protecting skin and improving healing ability. 17 Additionally, wound-healing materials need a swelling capability to absorb excess exudates, need oxygen permeability to permit respiration, and need the ability to eliminate of infection, 22 all of which are met by the LFFS, indicating its potential to promote wound healing. Second, the ability of LFFS to absorb BZL is essential for maintaining its antibacterial properties.…”

Error rate of automated calculation for wound surface area using a digital photography

“…In addition, the specialized feature for wound was applied to GVF snake algorithm to enhance the performance. All calculating procedures were fully automatically performed using the developed software tool …”

Automatic wound detection and size estimation using deep learning algorithms

“…3). This kind of wound/skin boundary segmentation is common among computational wound segmentation works [19][20][21][22][23]. Given our automatically generated cropped wound dataset (see "Detection" Section for details) we used the open-source software Labelme [26] to draw polygons around the wound edge for all the images in our dataset.…”

“…For example, wound measurement was performed in [20] by combining the computer vision and supervised image classification to detect wound edges and then segmentation of the tissues within the wound. In addition, the authors in [21] used wound images containing a color patch to analyze sequential changes in the wound. Furthermore, combining image segmentation, least-squares conformal mapping, and structure from motion, 3D transformation is utilized in [22] to measure wound area on a human body surface.…”