Diagnostic and Prognostic Utility of Non-Invasive Multimodal Imaging in Chronic Wound Monitoring: a Systematic Review

Automated chronic wounds medical assessment and tracking framework based on deep learning

Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.

Anthocyanin/Honey-Incorporated Alginate Hydrogel as a Bio-Based pH-Responsive/Antibacterial/Antioxidant Wound Dressing

Infection is a major problem that increases the normal pH of the wound bed and interferes with wound healing. Natural biomaterials can serve as a suitable environment to acquire a great practical effect on the healing process. In this context, anthocyanin-rich red cabbage (Brassica oleracea var. capitata F. rubra) extract and honey-loaded alginate hydrogel was fabricated using calcium chloride as a crosslinking agent. The pH sensitivity of anthocyanins can be used as an indicator to monitor possible infection of the wound, while honey would promote the healing process by its intrinsic properties. The mechanical properties of the hydrogel film samples showed that honey acts as a plasticizer and that increasing the incorporation from 200% to 400% enhances the tensile strength from 3.22 to 6.15 MPa and elongation at break from 0.69% to 4.75%. Moreover, a water absorption and retention study showed that the hydrogel film is able to absorb about 250% water after 50 min and retain 40% of its absorbed water after 12 h. The disk diffusion test showed favorable antibacterial activity of the honey-loaded hydrogel against both Gram-positive and Gram-negative Staphylococcus aureus and Escherichia coli, respectively. In addition, the incorporation of honey significantly improved the mechanical properties of the hydrogel. 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay proved the antioxidant activity of the honey and anthocyanin-containing hydrogel samples with more than 95% DPPH scavenging efficiency after 3 h. The pH-dependent property of the samples was investigated and recorded by observing the color change at different pH values of 4, 7, and 9 using different buffers. The result revealed a promising color change from red at pH = 4 to blue at pH = 7 and purple at pH = 9. An in vitro cell culture study of the samples using L929 mouse fibroblast cells showed excellent biocompatibility with significant increase in cell proliferation. Overall, this study provides a promising start and an antibacterial/antioxidant hydrogel with great potential to meet wound-dressing requirements.

Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review

Significance: Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the complex and dynamic processes involved in wound healing. Wound care teams capture images of wounds during clinical visits generating big datasets over time. Developing novel artificial intelligence (AI) systems can help clinicians diagnose, assess the effectiveness of therapy, and predict healing outcomes. Recent Advances: Rapid developments in computer processing have enabled the development of AI-based systems that can improve the diagnosis and effectiveness of therapy in various clinical specializations. In the past decade, we have witnessed AI revolutionizing all types of medical imaging like X-ray, ultrasound, computed tomography, magnetic resonance imaging, etc., but AI-based systems remain to be developed clinically and computationally for high-quality wound care that can result in better patient outcomes. Critical Issues: In the current standard of care, collecting wound images on every clinical visit, interpreting and archiving the data are cumbersome and time consuming. Commercial platforms are developed to capture images, perform wound measurements, and provide clinicians with a workflow for diagnosis, but AI-based systems are still in their infancy. This systematic review summarizes the breadth and depth of the most recent and relevant work in intelligent image-based data analysis and system developments for wound assessment. Future Directions: With increasing availabilities of massive data (wound images, wound-specific electronic health records, etc.) as well as powerful computing resources, AI-based digital platforms will play a significant role in delivering data-driven care to people suffering from debilitating chronic wounds.

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.

Interpretation of Near-Infrared Imaging in Acute and Chronic Wound Care

Vascular assessment is a critical component of wound care. Current routine noninvasive vascular studies have limitations which can give a false sense of security of the presence of adequate perfusion for healing. Near-infrared imaging modalities can serve as an additional diagnostic assessment of wounds in which adequate perfusion is a concern. Correct interpretation of near-infrared images obtained is critical as subtleties that exist in the acute and chronic wound population goes beyond the interpretation that increased signal is consistent with adequate perfusion for healing. The objective of this paper is to educate providers on the correct interpretation of this point-of-care imaging modality in day-to-day wound-care practice to guide clinical decision-making for rapid wound resolution.

Chronic wounds multimodal image database

A multimodal wound image database was created to allow fast development of computer-aided approaches for wound healing monitoring. The developed system with parallel camera optical axes enables multimodal images: photo, thermal, stereo, and depth map of the wound area to be acquired. As a result of using this system a multimodal database of chronic wound images is introduced. It contains 188 image sets of photographs, thermal images, and 3D meshes of the surfaces of chronic wounds acquired during 79 patient visits. Manual wound outlines delineated by an expert are also included in the dataset. All images of each case are additionally coregistered, and both numerical registration parameters and the transformed images are covered in the database. The presented database is publicly available for the research community at https://chronicwounddatabase.eu. That is the first publicly available database for evaluation and comparison of new image-based algorithms in the wound healing monitoring process with coregistered photographs, thermal maps, and 3D models of the wound area. Easily available database of coregistered multimodal data with the raw data set allows faster development of algorithms devoted to wound healing analysis and monitoring.

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

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. Two major tasks in wound analysis on images are segmentation of the wound from the healthy skin and background, and classification of the most important wound tissues like granulation, fibrin, and necrosis. These tasks are necessary for further assessment like wound measurement or healing evaluation based on tissue representation. Researchers use various methods and algorithms for image wound analysis with the aim to outperform accuracy rates and show the robustness of the proposed methods. Recently, neural networks and deep learning algorithms have driven considerable performance improvement across various fields, which has a led to a significant rise of research papers in the field of wound analysis as well. The aim of this paper is to provide an overview of recent methods for non-contact wound analysis which could be used for developing an end-to-end solution for a fully automated wound analysis system which would incorporate all stages from data acquisition, to segmentation and classification, ending with measurement and healing evaluation.

Physiological wound assessment from coregistered and segmented tissue hemoglobin maps

A handheld near-infrared optical scanner (NIROS) was recently developed to map for effective changes in oxy- and deoxyhemoglobin concentration in diabetic foot ulcers (DFUs) across weeks of treatment. Herein, a coregistration and image segmentation approach was implemented to overlay hemoglobin maps onto the white light images of ulcers. Validation studies demonstrated over 97% accuracy in coregistration. Coregistration was further applied to a healing DFU across weeks of healing. The potential to predict changes in wound healing was observed when comparing the coregistered and segmented hemoglobin concentration area maps to the visual area of the wound.

Imaging in Chronic Wound Diagnostics

Significance: Chronic wounds affect millions of patients worldwide, placing a huge burden on health care resources. Although significant progress has been made in the development of wound treatments, very few advances have been made in wound diagnosis. Recent Advances: Standard imaging methods like computed tomography, single-photon emission computed tomography, magnetic resonance imaging, terahertz imaging, and ultrasound imaging have been widely employed in wound diagnostics. A number of noninvasive optical imaging modalities like optical coherence tomography, near-infrared spectroscopy, laser Doppler imaging, spatial frequency domain imaging, digital camera imaging, and thermal and fluorescence imaging have emerged over the years. Critical Issues: While standard diagnostic wound imaging modalities provide valuable information, they cannot account for dynamic changes in the wound environment. In addition, they lack the capability to predict the healing outcome. Thus, there remains a pressing need for more efficient methods that can not only indicate the current state of the wound but also help determine whether the wound is on track to heal normally. Future Directions: Many imaging probes have been fabricated and shown to provide real-time assessment of tissue microenvironment and inflammatory responses in vivo. These probes have been demonstrated to noninvasively detect various changes in the wound environment, which include tissue pH, reactive oxygen species, fibrin deposition, matrix metalloproteinase production, and macrophage accumulation. This review summarizes the creation of these probes and their potential implications in wound monitoring.

Design and evaluation of an imager for assessing wound inflammatory responses and bioburden in a pig model

Our work details the development and characterization of a portable luminescence imaging device for detecting inflammatory responses and infection in skin wounds. The device includes a CCD camera and close-up lens integrated into a customizable 3D printed imaging chamber to create a portable light-tight imager for luminescence imaging. The chamber has an adjustable light portal that permits ample ambient light for white light imaging. This imager was used to quantify in real time the extent of two-dimensional reactive oxygen species (ROS) activity distribution using a porcine wound infection model. The imager was used to successfully visualize ROS-associated luminescent activities in vitro and in vivo. Using a pig full-thickness cutaneous wound model, we further demonstrate that this portable imager can detect the change of ROS activities and their relationship with vasculature in the wound environment. Finally, by analyzing ROS intensity and distribution, an imaging method was developed to distinguish infected from uninfected wounds. We discovered a distinct ROS pattern between bacteria-infected and control wounds corresponding to the microvasculature. The results presented demonstrate that this portable luminescence imager is capable of imaging ROS activities in cutaneous wounds in a large animal model, indicating suitability for future clinical applications.