Wound Size Imaging: Ready for Smart Assessment and Monitoring

Self-supporting wound care mobile applications for nurses: A scoping review

AIM

This study provides an overview of the literature to identify and map the types of available evidence on self-supporting mobile applications used by nurses in wound care regarding their development, evaluation and outcomes for patients, nurses and the healthcare system.

DESIGN

Scoping review.

REVIEW METHOD

Joanna Briggs Institute scoping review methodology was used.

DATA SOURCES

A search was performed using MEDLINE, Embase, CINAHL (via EBSCO), Web of Science, LiSSa (Littérature Scientifique en Santé), Cochrane Wounds, Érudit and grey literature, between April and October 2022, updated in April 2023, to identify literature published in English and French.

RESULTS

Eleven studies from 14 publications met the inclusion criteria. Mostly descriptive, the included studies presented mobile applications that nurses used, among other things, to assess wounds and support clinical decision-making. The results described how nurses were iteratively involved in the process of developing and evaluating mobile applications using various methods such as pilot tests. The three outcomes most frequently reported by nurses were as follows: facilitating care, documentation on file and access to evidence-based data.

CONCLUSION

The potential of mobile applications in wound care is within reach. Nurses are an indispensable player in the successful development of these tools.

IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE

If properly developed and evaluated, mobile applications for wound care could enhance nursing practices and improve patient care. The development of ethical digital competence must be ensured during initial training and continued throughout the professional journey.

IMPACT

We identified a dearth of studies investigating applications that work without Internet access. More research is needed on the development of mobile applications in wound care and their possible impact on nursing practice in rural areas and the next generation of nurses.

REPORTING METHOD

The Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Review guidelines were used.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities

With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time

Since the 1970s, artificial intelligence (AI) has played an increasingly pivotal role in the medical field, enhancing the efficiency of disease diagnosis and treatment. Amidst an aging population and the proliferation of chronic disease, the prevalence of complex surgeries for high-risk multimorbid patients and hard-to-heal wounds has escalated. Healthcare professionals face the challenge of delivering safe and effective care to all patients concurrently. Inadequate management of skin wounds exacerbates the risk of infection and complications, which can obstruct the healing process and diminish patients' quality of life. AI shows substantial promise in revolutionizing wound care and management, thus enhancing the treatment of hospitalized patients and enabling healthcare workers to allocate their time more effectively. This review details the advancements in applying AI for skin wound assessment and the prediction of healing timelines. It emphasizes the use of diverse algorithms to automate and streamline the measurement, classification, and identification of chronic wound healing stages, and to predict wound healing times. Moreover, the review addresses existing limitations and explores future directions.

Inter-rater reliability among healthcare professionals in assessing postoperative wound photos for the presence or absence of surgical wound dehiscence: A Pretest - Posttest study

BACKGROUND

Surgical wound dehiscence (SWD) has various definitions, which complicates accurate and uniform diagnosis. To address this, the World Union Wound Healing Societies (WUWHS) presented a consensus based definition and classification for SWD (2018).

AIM

This quasi-experimental pretest-posttest study investigates the inter-rater reliability among healthcare professionals (HCP) and wound care professionals (WCP) when assessing wound photos on the presence or absence of SWD before and after training on the WUWHS-definition.

METHODS

Wound expert teams compiled a set of twenty photos (SWD+: nineteen, SWD-: one), and a video training. Subsequently, 262 healthcare professionals received the pretest link to assess wound photos. After completion, participants received the posttest link, including a (video) training on the WUWHS-definition, and reassessment of fourteen photos (SWD+: thirteen, SWD-: one).

PRIMARY OUTCOMES

1) pretest-posttest inter-rater-reliability among participants in assessing photos in congruence with the WUWHS-definition 2) the impact of training on assessment scores.

SECONDARY OUTCOME

familiarity with the WUWHS-definition.

RESULTS

One hundred thirty-one participants (65 HCPs, 66 WCPs) completed both tests. The posttest inter-rater reliability among participants for correctly identifying SWD was increased from 67.6 % to 76.2 %, reaching statistical significance (p-value: 0.001; 95 % Confidence Interval [1.8-2.2]). Sub-analyses per photo showed improved SWD posttest scores in thirteen photos, while statistical significance was reached in seven photos. Thirty-three percent of participants knew the WUWHS-definition.

CONCLUSION

The inter-rater reliability among participants increases after training on the WUWHS-definition. The definition provides diagnostic criteria for accurate SWD diagnosis. Widespread use of the definition may improve uniformity in care for patients with SWD.

Content of a wound care mobile application for newly graduated nurses: an e-Delphi study

BACKGROUND

Wound care represents a considerable challenge, especially for newly graduated nurses. The development of a mobile application is envisioned to improve knowledge transfer and facilitate evidence-based practice. The aim of this study was to establish expert consensus on the initial content of the algorithm for a wound care mobile application for newly graduated nurses.

METHODS

Experts participated in online surveys conducted in three rounds. Twenty-nine expert wound care nurses participated in the first round, and 25 participated in the two subsequent rounds. The first round, which was qualitative, included a mandatory open-ended question solicitating suggestions for items to be included in the mobile application. The responses underwent content analysis. The subsequent two rounds were quantitative, with experts being asked to rate their level of agreement on a 5-point Likert scale. These rounds were carried out iteratively, allowing experts to review their responses and see anonymized results from the previous round. We calculated the weighted kappa to determine the individual stability of responses within-subjects between the quantitative rounds. A consensus threshold of 80% was predetermined.

RESULTS

In total, 80 items were divided into 6 categories based on the results of the first round. Of these, 75 (93.75%) achieved consensus during the two subsequent rounds. Notably, 5 items (6.25%) did not reach consensus. The items with the highest consensus related to the signs and symptoms of infection, pressure ulcers, and the essential elements for healing. Conversely, items such as toe pressure measurement, wounds around drains, and frostbite failed to achieve consensus.

CONCLUSIONS

The results of this study will inform the development of the initial content of the algorithm for a wound care mobile application. Expert participation and their insights on infection-related matters have the potential to support evidence-based wound care practice. Ongoing debates surround items without consensus. Finally, this study establishes expert wound care nurses' perspectives on the competencies anticipated from newly graduated nurses.

Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing

OBJECTIVE

Blood circulation is an important indicator of wound healing. In this study, a tissue oxygen saturation detecting (TOSD) system that is based on multispectral imaging (MSI) is proposed to quantify the degree of tissue oxygen saturation (StO2) in cutaneous tissues.

METHODS

A wound segmentation algorithm is used to segment automatically wound and skin areas, eliminating the need for manual labeling and applying adaptive tissue optics. Animal experiments were conducted on six mice in which they were observed seven times, once every two days. The TOSD system illuminated cutaneous tissues with two wavelengths of light - red ([Formula: see text] nm) and near-infrared ([Formula: see text] nm), and StO2 levels were calculated using images that were captured using a monochrome camera. The wound segmentation algorithm using ResNet34-based U-Net was integrated with computer vision techniques to improve its performance.

RESULTS

Animal experiments revealed that the wound segmentation algorithm achieved a Dice score of 93.49%. The StO2 levels that were determined using the TOSD system varied significantly among the phases of wound healing. Changes in StO2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. Moreover, statistical features that were extracted from the TOSD system and LSCI were utilized in principal component analysis (PCA) to visualize different wound healing phases. The average silhouette coefficients of the TOSD system with segmentation (ResNet34-based U-Net) and LSCI were 0.2890 and 0.0194, respectively.

CONCLUSION

By detecting the StO2 levels of cutaneous tissues using the TOSD system with segmentation, the phases of wound healing were accurately distinguished. This method can support medical personnel in conducting precise wound assessments. Clinical and Translational Impact Statement-This study supports efforts in monitoring StO2 levels, wound segmentation, and wound healing phase classification to improve the efficiency and accuracy of preclinical research in the field.

Measurement of perineal tears as an additional tool for laceration assessment during vaginal birth

INTRODUCTION

Spontaneous lacerations at vaginal birth are everyday events, but their classification and management still challenge midwifery care. This study aims to measure and describe first-degree and second-degree perineal lacerations resulting from vaginal birth, describe their repair, and the education provided for care.

METHODS

A descriptive study was conducted in a public maternity hospital in São Paulo, Brazil, with 87 parturients. Data were collected between October 2017 and June 2018 using a structured instrument containing obstetric variables and a description of lacerations. The obstetricians and nurse midwives assisted with births, determining the degree of laceration and intervention, and the researchers measured and reported them.

RESULTS

The majority of parturients (82.7%) had lacerations only in the anterior region, 8% had them in the posterior region, and 9.2% in both regions. The lacerations were classified as first-degree (78.1%) or second-degree (21.8%). Among the 32 nulliparous parturients, 27.6% had first-degree lacerations, and 9.2% had second-degree. Of the 55 multiparous parturients, 50.6% had first-degree, and 12.6% had second-degree. Among the lacerations assessed as first-degree, 25% had deeper tissue layers compromised in addition to the skin and mucosa. There were 180 lacerations, with an average length of 33.1 mm, depth of 19.8 mm, and width of 23.8 mm. Half of the parturients did not receive guidance on laceration care. There was no association between parity and size, number, location, or degree classification of lacerations.

CONCLUSIONS

This study provides a broad description of the characteristics of perineal lacerations and presents measurement techniques as a complementary resource for evaluating lacerations.

A Novel Randomized Trial Protocol for Evaluating Wound Healing Interventions

Background: Randomized controlled trials using complete healing as an endpoint suffer from poor statistical power, owing to the heterogeneity of wounds and their healing trajectories. The Food and Drug Administration (FDA) has recently consulted with expert groups to consider percentage area reduction (PAR) of the wound over a 4-week period as a valid intermediate endpoint, creating the opportunity for more powerful study designs. Methods: A within-subject controlled study design comparing the PAR of venous leg ulcers (VLU) in patients over 4 weeks receiving different interventions. Twenty-nine patients received multilayer compression over 4 weeks, followed by neuromuscular electrostimulation (NMES) of the leg muscle pump in addition to compression for a further 4 weeks. Paired comparison was then made of PAR between the two phases. A second cohort of 22 patients received only multilayer compression throughout both 4-week phases. Results: Patients randomized to NMES saw a significant increase in healing rate compared with compression alone, whereas patients receiving compression only saw no significant change in healing rate throughout the course of the study. Conclusions: Intermittent NMES of the common peroneal nerve significantly accelerates the healing of VLU. It is well tolerated by patients and deserves serious consideration as an adjuvant to compression therapy. PAR is a useful metric for comparing the performance of wound healing interventions, and the self-controlled trial design allows sensitive discrimination with a relatively small number of subjects over a reasonably short trial period. The study is reported according to the CONSORT reporting guidelines. Clinical Trial Registration: NCT03396731 (ClinicalTrials.gov).

A new perspective on quantitative assessment of photodynamic therapy mediated hydrogel nanocomposite in wound healing using objective biospeckle and morphological local-gradient

Skin wounding is a serious public health issue, especially when considering factors that accelerate tissue recovery. Consequently, the use of photodynamic therapy (PDT) as an effective wound-healing treatment has attracted more scientific attention. Although assessing the wound healing rate is crucial for appropriate monitoring of the probability of wound healing and evaluating the treatment efficiency, the currently used techniques lack the ability to provide such information. Therefore, this study has two aims, first, it contributes to the development of a new image-guided biospeckle system for quantitative monitoring of skin wound healing rate. Second, it evaluates the potential of using a novel synthesized PDT-mediated polyethylene glycol fabric with methylene blue (PEG-MB) hydrogel nanocomposite in accelerating wound healing. The proposed imaging system initially acquires raw biospeckle images from the wound regions of adult healthy albino mice treated with the synthesized hydrogel nanocomposite. Each raw biospeckle image is then converted into maps of morphological local-gradient matrices implemented from the combination of dilation and erosion operations at different radii up to 25 pixels. Subsequently, their intensity histogram statistics are computed, taking central moments as the feature set. Final characterization is achieved via a linear combination of the biospeckle statistics maintaining as much variance as possible using principal component analysis (PCA). The results confirmed by cytokine concentration measurement and histological investigation demonstrate that the innovative biospeckle image-guided system is ideal for investigating wound healing and suggest the potential of the hydrogel nanocomposite as an active dressing.

Temporal Evaluation of the Surface Area of Treated Skin Ulcers Caused by Cutaneous Leishmaniasis and Relation with Optical Parameters in an Animal Model: A Proof of Concept

Cutaneous leishmaniasis (CL) is a neglected disease caused by an intracellular parasite of the Leishmania genus. CL lacks tools that allow its understanding and treatment follow-up. This article presents the use of metrical and optical tools for the analysis of the temporal evolution of treated skin ulcers caused by CL in an animal model. Leishmania braziliensis and L. panamensis were experimentally inoculated in golden hamsters, which were treated with experimental and commercial drugs. The temporal evolution was monitored by means of ulcers' surface areas, as well as absorption and scattering optical parameters. Ulcers' surface areas were obtained via photogrammetry, which is a procedure that allowed for 3D modeling of the ulcer using specialized software. Optical parameters were obtained from a spectroscopy study, representing the cutaneous tissue's biological components. A one-way ANOVA analysis was conducted to identify relationships between both the ulcers' areas and optical parameters. As a result, ulcers' surface areas were found to be related to the following optical parameters: epidermis thickness, collagen, keratinocytes, volume-fraction of blood, and oxygen saturation. This study is a proof of concept that shows that optical parameters could be associated with metrical ones, giving a more reliable concept during the assessment of a skin ulcer's healing.

Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset.

Comparison of area measurement methods in the routine assessment of diabetic foot ulcers-A consistency analysis method

BACKGROUND

Ulcer area is a critical parameter in diabetic foot ulcer assessment but existing methods have deficiencies for routine measurement.

AIM

We hypothesized that the Image J-based Computer Analysis method has a high level of agreement with the commonly used Maximum Length and Width and the Transparent Dressing-based Square Grid methods and aimed to test the consistency and verify the feasibility of the Image J-based Computer Analysis method in the routine assessment of ulcers.

METHODS

Outpatient attendees with diabetic foot ulcers at the Department of Endocrinology of Sun Yat-sen Memorial Hospital were enrolled between October 2020 and October 2021. The three methods sequentially assessed the area of 65 included ulcers. Results were analysed using one-way analysis of variance and Bland-Altman plots to perform consistency analysis.

RESULTS

The mean ± standard deviation ulcer area measured using the three methods were 14.79 ± 5.39, 14.35 ± 5.26, and 14.30 ± 5.26 cm² , respectively. The measurement differences among the three groups or between any two were not statistically significant. Bland-Altman plots showed good consistency between the Image J-based Computer Analysis and the other two methods.

CONCLUSION

The Image J-based Computer Analysis method can be interchanged with the other methods to assess ulcer areas. It is freely accessible, accurate and home-operable, thus worth consideration by nurses for routine ulcer area assessment.

Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera

Pressure injuries are a common problem resulting in poor prognosis, long-term hospitalization, and increased medical costs in an aging society. This study developed a method to do automatic segmentation and area measurement of pressure injuries using deep learning models and a light detection and ranging (LiDAR) camera. We selected the finest photos of patients with pressure injuries, 528 in total, at National Taiwan University Hospital from 2016 to 2020. The margins of the pressure injuries were labeled by three board-certified plastic surgeons. The labeled photos were trained by Mask R-CNN and U-Net for segmentation. After the segmentation model was constructed, we made an automatic wound area measurement via a LiDAR camera. We conducted a prospective clinical study to test the accuracy of this system. For automatic wound segmentation, the performance of the U-Net (Dice coefficient (DC): 0.8448) was better than Mask R-CNN (DC: 0.5006) in the external validation. In the prospective clinical study, we incorporated the U-Net in our automatic wound area measurement system and got 26.2% mean relative error compared with the traditional manual method. Our segmentation model, U-Net, and area measurement system achieved acceptable accuracy, making them applicable in clinical circumstances.

Computerised prediction of healing for venous leg ulcers

Early prediction of delayed healing for venous leg ulcers could improve management outcomes by enabling earlier initiation of adjuvant therapies. In this paper, we propose a framework for computerised prediction of healing for venous leg ulcers assessed in home settings using thermal images of the 0 week. Wound data of 56 older participants over 12 weeks were used for the study. Thermal images of the wounds were collected in their homes and labelled as healed or unhealed at the 12th week follow up. Textural information of the thermal images at week 0 was extracted. Thermal images of unhealed wounds had a higher variation of grey tones distribution. We demonstrated that the first three principal components of the textural features from one timepoint can be used as an input to a Bayesian neural network to discriminate between healed and unhealed wounds. Using the optimal Bayesian neural network, the classification results showed 78.57% sensitivity and 60.00% specificity. This non-contact method, incorporating machine learning, can provide a computerised prediction of this delay in the first assessment (week 0) in participants' homes compared to the current method that is able to do this in 3rd week and requires contact digital planimetry.

Interactive Medical Image Labeling Tool to Construct a Robust Convolutional Neural Network Training Data Set: Development and Validation Study

Background

Skin ulcers are an important cause of morbidity and mortality everywhere in the world and occur due to several causes, including diabetes mellitus, peripheral neuropathy, immobility, pressure, arteriosclerosis, infections, and venous insufficiency. Ulcers are lesions that fail to undergo an orderly healing process and produce functional and anatomical integrity in the expected time. In most cases, the methods of analysis used nowadays are rudimentary, which leads to errors and the use of invasive and uncomfortable techniques on patients. There are many studies that use a convolutional neural network to classify the different tissues in a wound. To obtain good results, the network must be trained with a correctly labeled data set by an expert in wound assessment. Typically, it is difficult to label pixel by pixel using a professional photo editor software, as this requires extensive time and effort from a health professional.

Objective

The aim of this paper is to implement a new, fast, and accurate method of labeling wound samples for training a neural network to classify different tissues.

Methods

We developed a support tool and evaluated its accuracy and reliability. We also compared the support tool classification with a digital gold standard (labeling the data with an image editing software).

Results

The obtained comparison between the gold standard and the proposed method was 0.9789 for background, 0.9842 for intact skin, 0.8426 for granulation tissue, 0.9309 for slough, and 0.9871 for necrotic. The obtained speed on average was 2.6, compared to that of an advanced image editing user.

Conclusions

This method increases tagging speed on average compared to an advanced image editing user. This increase is greater with untrained users. The samples obtained with the new system are indistinguishable from the samples made with the gold standard.

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.

Reliability of a Three-Dimensional Wound Camera and Correlation With Routine Ruler Measurement in Diabetes-Related Foot Ulceration

BACKGROUND

In an era of increasing technology and telehealth utilization, three-dimensional (3D) wound cameras promise reliable, rapid, and touch-free ulceration measurements. However, reliability data for commercially available devices in the diabetes foot service setting is lacking. We aimed to evaluate the reliability of diabetes-related foot ulceration measurement using a 3D wound camera in comparison to the routinely used ruler and probe.

METHOD

Participants were prospectively recruited from a tertiary interdisciplinary diabetes foot service. Ulcerations were measured at each visit by two blinded observers, first by ruler and probe, and then using a 3D wound camera twice. Reliability was evaluated using intraclass correlation coefficients (ICC). Measurement methods were compared by Pearson correlation.

RESULTS

Sixty-three ulcerations affecting 38 participants were measured over 122 visits. Interobserver reliability of ruler measurement was excellent for estimated area (ICC 0.98, 95% CI 0.97-0.98) and depth (ICC 0.93, 95% CI 0.90-0.95). Intraobserver and interobserver reliability of the 3D wound camera area was excellent (ICC 0.96, 95%CI 0.95-0.97 and 0.97 95% CI 0.96-0.98, respectively). Depth was unrecordable in over half of 3D wound camera measurements, and reliability was inferior to probe measurement. Area correlation between methods was good (R = 0.88 and 0.94 per observer); however, depth correlation was poor (R = 0.49 and 0.65).

CONCLUSIONS

3D wound cameras offer practical advantages over ruler-based measurement. In diabetes-related foot ulceration, the reliability and comparability of area measurement was excellent across both methods, although depth was more reliably obtained by the probe. These limitations, together with cost, are important considerations if implementing this technology in diabetes foot care.

Topical odour management in burn patients

Preventing microbial colonization or infections that cause offensive smells may lead to odor reduction. As both anaerobic and aerobic bacteria cause the release of malodor from wounds, the most direct way of avoiding or eliminating wound odor is to prevent or eradicate the responsible infection through the debridement of necrotic tissues. However, some burn patients with malodorous wounds are unable to undergo debridement due to systemic conditions, especially in the acute stage. Moreover, the optimal drug doses and dressings to ensure the efficacy and cost-effectiveness of odorous burn wound management is unclear. The purpose of this commentary is to outline the odor management options available for burn patients, focusing on topical strategies. Numerous potential therapies for treating odorous wounds after burn injuries are suggested.

Experimental Study on Wound Area Measurement with Mobile Devices

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.