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

Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review

It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research.

Automatic wound detection and size estimation using deep learning algorithms

Evaluating and tracking wound size is a fundamental metric for the wound assessment process. Good location and size estimates can enable proper diagnosis and effective treatment. Traditionally, laboratory wound healing studies include a collection of images at uniform time intervals exhibiting the wounded area and the healing process in the test animal, often a mouse. These images are then manually observed to determine key metrics -such as wound size progress- relevant to the study. However, this task is a time-consuming and laborious process. In addition, defining the wound edge could be subjective and can vary from one individual to another even among experts. Furthermore, as our understanding of the healing process grows, so does our need to efficiently and accurately track these key factors for high throughput (e.g., over large-scale and long-term experiments). Thus, in this study, we develop a deep learning-based image analysis pipeline that aims to intake non-uniform wound images and extract relevant information such as the location of interest, wound only image crops, and wound periphery size over-time metrics. In particular, our work focuses on images of wounded laboratory mice that are used widely for translationally relevant wound studies and leverages a commonly used ring-shaped splint present in most images to predict wound size. We apply the method to a dataset that was never meant to be quantified and, thus, presents many visual challenges. Additionally, the data set was not meant for training deep learning models and so is relatively small in size with only 256 images. We compare results to that of expert measurements and demonstrate preservation of information relevant to predicting wound closure despite variability from machine-to-expert and even expert-to-expert. The proposed system resulted in high fidelity results on unseen data with minimal human intervention. Furthermore, the pipeline estimates acceptable wound sizes when less than 50% of the images are missing reference objects.

Application of secondary intention for the restoration of the apical triangle after Mohs micrographic surgery

BACKGROUND

Restoring the apical triangle (AT) to maintain the symmetry of the face after Mohs micrographic surgery can be challenging. We have applied secondary intention (SI) after partial closure in cases with large defects.

OBJECTIVE

To compare the cosmetic results between immediate closure (IC) and SI.

METHODS & MATERIALS

We retrospectively reviewed 24 patients (IC group: n = 15, SI group: n = 9). To evaluate the symmetry, the comparative ratio (CR; area of the AT of the involved side/area of the normal contralateral side) was calculated.

RESULTS

The defects were significantly larger in the SI group than in the IC group (588.89 ± 346.53 mm² vs. 252.87 ± 196.52 mm², p < .01). While there was no statistically significant difference in average CR, the standard deviation was higher in the SI group (0.95 ± 0.08 vs. 0.93 ± 0.31, p = .3359), indicating the difficulty in predicting the results of SI healing. The average visual analog scale score evaluated by two dermatologists was higher in the IC group, albeit without a significant difference (8.23 ± 0.96 vs. 7.78 ± 1.52, p = .5267).

CONCLUSION

SI after partial closure can be an option for large defects in the AT area.

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

Introduction

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most predominant and fatal pathogens at wound infection sites. MRSA is difficult to treat because of its antibiotic resistance and ability to form biofilms at the wound site.

Methods

In this study, a novel nanoscale liquid film-forming system (LFFS) loaded with benzalkonium bromide was produced based on polyvinyl alcohol and chitosan.

Results

This LFFS showed a faster and more potent effect against MRSA252 than benzalkonium bromide aqueous solution both in vitro and in vivo. Additionally, the LFFS had a stronger ability to destroy biofilms (5 mg/mL) and inhibit their formation (1.33 μg/mL). The LFFS inflicted obvious damage to the structure and integrity of MRSA cell membranes and caused increases in the release of alkaline phosphate and lactate dehydrogenase in the relative electrical conductivity and in K⁺ and Mg²⁺ concentrations due to changes in the MRSA cell membrane permeability.

Conclusion

The novel LFFS is promising as an effective system for disinfectant delivery and for application in the treatment of MRSA wound infections.

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

BACKGROUND

Although measuring would size using digital photography is a quick and simple method to evaluate the skin wound, the possible compatibility of it has not been fully validated.

PURPOSE

To investigate the error rate of our newly developed wound surface area calculation using digital photography.

METHODS

Using a smartphone and a digital single lens reflex (DSLR) camera, four photographs of various sized wounds (diameter: 0.5-3.5 cm) were taken from the facial skin model in company with color patches. The quantitative values of wound areas were automatically calculated. The relative error (RE) of this method with regard to wound sizes and types of camera was analyzed.

RESULTS

RE of individual calculated area was from 0.0329% (DSLR, diameter 1.0 cm) to 23.7166% (smartphone, diameter 2.0 cm). In spite of the correction of lens curvature, smartphone has significantly higher error rate than DSLR camera (3.9431±2.9772 vs 8.1303±4.8236). However, in cases of wound diameter below than 3 cm, REs of average values of four photographs were below than 5%. In addition, there was no difference in the average value of wound area taken by smartphone and DSLR camera in those cases.

CONCLUSION

For the follow-up of small skin defect (diameter: <3 cm), our newly developed automated wound area calculation method is able to be applied to the plenty of photographs, and the average values of them are a relatively useful index of wound healing with acceptable error rate.