A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis

DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring

Introduction

Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic methods are invasive, time-consuming, and costly. There is a need for non-invasive, efficient, and affordable solutions in diabetic foot care.

Methods

We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. Additionally, deep-learning models were implemented to classify infection and ischemia in DFUs. The preliminary performance of the platform was tested on wound images acquired using a cell phone.

Results

DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. The system successfully measured wound size and performed tissue color and textural analysis for a comparative assessment of macroscopic wound features. In clinical testing, DFUCare localized wounds and predicted infected and ischemic with an error rate of less than 10%, underscoring the strong performance of the platform.

Discussion

DFUCare presents an innovative approach to wound care, offering a cost-effective, remote, and convenient healthcare solution. By enabling non-invasive and accurate analysis of wounds using mobile devices, this platform has the potential to revolutionize diabetic foot care and improve clinical outcomes through early detection of infection and ischemia.

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.

Application of multiple deep learning models for automatic burn wound assessment

PURPOSE

Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to estimate the size of an irregular shape by inspection. Many articles reported the discrepancy of estimating %TBSA burned by different doctors. We set up a system with multiple deep learning (DL) models for %TBSA estimation, as well as the segmentation of possibly poor-perfused deep burn regions from the entire wound.

METHODS

We proposed boundary-based labeling for datasets of total burn wound and palm, whereas region-based labeling for the dataset of deep burn wound. Several powerful DL models (U-Net, PSPNet, DeeplabV3+, Mask R-CNN) with encoders ResNet101 had been trained and tested from the above datasets. With the subject distances, the %TBSA burned could be calculated by the segmentation of total burn wound area with respect to the palm size. The percentage of deep burn area could be obtained from the segmentation of deep burn area from the entire wound.

RESULTS

A total of 4991 images of early burn wounds and 1050 images of palms were boundary-based labeled. 1565 out of 4994 images with deep burn were preprocessed with superpixel segmentation into small regions before labeling. DeeplabV3+ had slightly better performance in three tasks with precision: 0.90767, recall: 0.90065 for total burn wound segmentation; precision: 0.98987, recall: 0.99036 for palm segmentation; and precision: 0.90152, recall: 0.90219 for deep burn segmentation.

CONCLUSION

Combining the segmentation results and clinical data, %TBSA burned, the volume of fluid for resuscitation, and the percentage of deep burn area can be automatically diagnosed by DL models with a pixel-to-pixel method. Artificial intelligence provides consistent, accurate and rapid assessments of burn wounds.

Laser in the Treatment of Atonic Wounds

Atonic wounds represent a major health problem, being frequently encountered in medical practice with consequences that have a negative impact on the patient's daily life as well as their general condition. In this study, a brand laser with a 12-watt probe was used to stimulate patients' wounds. We involved in this study a group of 65 patients, which was compared with a group of 30 patients, the latter not receiving this laser therapy. The data were accumulated from the questionnaire of subjective assessment of the laser impact on patients' condition as well as from the local evolution. We noticed the improvement of the local symptomatology which was found to be more effective in the patients from the study group compared to the reference group. The beneficial and positive effects, mainly on the symptoms but also on the local evolution of atonic wounds, can be observed in our study. We consider that this therapy is of major importance considering the lower costs both from the shortening of hospitalization and the long-term use of various substances. The early reintegration of patients into daily life is an important benefit for them.

2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System

Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complications and thus shorten the wound healing process. This paper describes an automatic wound segmentation based on a wound recording system built upon a 7-DoF robot arm with an attached RGB-D camera and high-precision 3D scanner. The developed system represents a novel combination of 2D and 3D segmentation, where the 2D segmentation is based on the MobileNetV2 classifier and the 3D component is based on the active contour model, which works on the 3D mesh to further refine the wound contour. The end output is the 3D model of only the wound surface without the surrounding healthy skin and geometric parameters in the form of perimeter, area, and volume.

AI-Assisted Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a Smartphone

Wound assessment is essential for evaluating wound healing. One cornerstone of wound care practice is the use of clinical guidelines that mandate regular documentation, including wound size and wound tissue composition, to determine the rate of wound healing. The traditional method requires wound care professionals to manually measure the wound area and tissue composition, which is time-consuming, costly, and difficult to reproduce. In this work, we propose an approach for automatic wound assessment that incorporates automatic color and measurement calibration and artificial intelligence algorithms. Our approach enables the comparison of images taken at different times, even if they were taken under different lighting conditions, distances, lenses, and camera sensors. We designed a calibration chart and developed automatic algorithms for color and measurement calibration. The wound area and wound composition on the images were annotated by three physicians with more than ten years of experience. Deep learning models were then developed to mimic what the physicians did on the images. We examined two network variants, U-Net with EfficientNet and U-Net with MobileNetV2, on wound images with a size of 1024 × 1024 pixels. Our best-performing algorithm achieved a mean intersection over union (IoU) of 0.6964, 0.3957, 0.6421, and 0.1552 for segmenting a wound area, epithelialization area, granulation tissue, and necrotic tissue, respectively. Our approach was able to accurately segment the wound area and granulation tissue but was inconsistent with respect to the epithelialization area and necrotic tissue. The calibration chart, which helps calibrate colors and scales, improved the performance of the algorithm. The approach could provide a thorough assessment of the wound, which could help clinicians tailor treatment to the patient's condition.

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Introduction

Surgical Site Infection (SSI) is a common healthcare-associated infection that imposes a considerable clinical and economic burden on healthcare systems. Advances in wearable sensors and digital technologies have unlocked the potential for the early detection and diagnosis of SSI, which can help reduce this healthcare burden and lower SSI-associated mortality rates.

Methods

In this study, we evaluated the ability of a multi-modal bio-signal system to predict current and developing superficial incisional infection in a porcine model infected with Methicillin Susceptible Staphylococcus Aureus (MSSA) using a bagged, stacked, and balanced ensemble logistic regression machine learning model.

Results

Results demonstrated that the expression levels of individual biomarkers (i.e., peri-wound tissue oxygen saturation, temperature, and bioimpedance) differed between non-infected and infected wounds across the study period, with cross-correlation analysis indicating that a change in bio-signal expression occurred 24 to 31 hours before this change was reflected by clinical wound scoring methods employed by trained veterinarians. Moreover, the multi-modal ensemble model indicated acceptable discriminability to detect the presence of a current superficial incisional SSI (AUC = 0.77), to predict an SSI 24 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.80), and to predict an SSI 48 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.74).

Discussion

In sum, the results of the current study indicate that non-invasive multi-modal sensor and signal analysis systems have the potential to detect and predict superficial incisional SSIs in porcine subjects under experimental conditions.

Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis

A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.

Chronic Wound Telemedicine Models Before and During the COVID-19 Pandemic: A Scoping Review

GENERAL PURPOSE

To present the results of a scoping review exploring chronic wound care telemedicine before and during the pandemic, including the characteristics of the models implemented.

TARGET AUDIENCE

This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care.

LEARNING OBJECTIVES/OUTCOMES

After participating in this educational activity, the participant will:1. Identify the characteristics of the studies the authors examined for their scoping review of chronic wound care telemedicine.2. Choose the electronic methods commonly used for wound care telemedicine in the studies the authors examined.3. Recognize the implications for the patients who participated in chronic wound care telemedicine in the studies the authors examined.

ABSTRACT

OBJECTIVETo explore different chronic wound telemedicine models and identify current research on this topic.METHODSThe authors searched the MEDLINE and EMBASE databases on August 10, 2021 and identified 58 articles included in the analysis.RESULTSIncluded studies were published between 1999 and 2021, with more than half of the studies published between 2015 to 2019 (25.9%, n = 15/58) and 2020 to 2021 (25.9%, n = 15/58). There were 57 models identified, of which 87.7% (n = 50/57) used a blended model of care. Image assessment was the most common element in blended care (66.0%, n = 33/50), followed by video consultation (46.0%, n = 23/50), text (44.0%, n = 22/50), and telephone consultation (22.0%, n = 11/50). Purely virtual care was used in 12.3% (n = 7/57) of models, 85.7% (n = 6/7) of which were implemented during the COVID-19 pandemic. Most studies conducted a quantitative analysis (62.1%, n = 36/58); 20.7% (n = 12/58) conducted a qualitative analysis, and 17.2% (n = 10/58) conducted both. The most frequently assessed results were wound outcomes (53.4%, n = 31/58) and patient opinions (25.9%, n = 15/58).CONCLUSIONSChronic wound care-related telemedicine has common elements: image assessment, video and telephone consultation, and text-based information that can be combined in a variety of ways with unique implementation barriers. Blended care models are more common than purely virtual alternatives. Heterogeneity among outcomes and reporting methods make the results difficult to synthesize.

Wound Detection by Simple Feedforward Neural Network

Chronic wounds are a heavy burden on medical facilities, so any help in treating them is most welcome. Current research focuses on wound analysis, especially wound tissue classification, wound measurement, and wound healing prediction to assist medical personnel in wound treatment, with the main goal of reducing wound healing time. The first phase of wound analysis is wound segmentation, where the task is to extract wounds from the healthy tissue and image background. In this work, a standard feedforward neural network was developed for the purpose of wound segmentation using data from the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge. It proved to be a simple yet efficient method for extracting wounds from images. The proposed algorithm is part of a compact system that analyzes chronic wounds using a robotic manipulator, RGB-D camera and 3D scanner. The feedforward neural network consists of only five fully connected layers, the first four with Rectified Linear Unit (ReLU) activation functions and the last with sigmoid activation functions. Three separate models were trained and tested using images provided as part of the challenge. The predicted images were post-processed and merged to improve the final segmentation performance.The accuracy metrics observed during model training and selection were Precision, Recall and F1 score. The experimental results of the proposed network provided a recall value of 0.77, precision value of 0.72, and an F1 score (Dice score) of 0.74.

Automatic Robot-Driven 3D Reconstruction System for Chronic Wounds

Chronic wounds, or wounds that are not healing properly, are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. This paper describes an automatic wound recording system build upon 7 DoF robot arm with attached RGB-D camera and high precision 3D scanner. The developed system presents a novel NBV algorithm that utilizes surface-based approach based on surface point density and discontinuity detection. The system was evaluated on multiple wounds located on medical models as well as on real patents recorded in clinical medical center.

Content validation and usability of a chatbot of guidelines for wound dressing

INTRODUCTION

The growing demand from patients with wounds from various causes has significantly challenged primary care nurses. A chatbot, with duly validated evidence-based content, can assist both nurses and patients in managing care. This work describes the development process of such a chatbot (BOTCURATIVO) that aims to help the treatment of wounds by non-specialists, by giving guidelines about the recommended wound dressing procedures for each type of wound.

METHOD

Methodological research was carried out in three phases. The first one corresponded to the validation of the script's content through a panel of enterostomal therapist nurses, who evaluated the domains and items of the chatbot script. Data analysis was performed using the Content Validity Index by individual level and scale level (≥ 0.80). To verify the agreement between the evaluators, the Kappa test was used. In the second phase, the chatbot was developed, using GOOGLE'S DIALOGFLOW platform. Finally, in the third phase, the chatbot's usability was analyzed using the System Usability Scale (SUS), by 17 users, 8 of them being patients with chronic wounds, 5 caregivers of people with acute and chronic wounds and 4 nurses.

RESULTS

The established domains achieved excellent suitability, relevance and representativeness criteria, all above 90 %; the content validity index per level of scale reached 0.97 and 0.82 by the methods of average and universal agreement, respectively, with excellent agreement between the evaluators (Kappa value: 0.83). The global usability score was 80.1.

CONCLUSION

The script developed and incorporated into the chatbot prototype achieved a satisfactory level of content validity. The usability of the chatbot was considered good, adding to the credibility of the device.