Chronic wound assessment and infection detection method

Slow-sculpting graphene oxide/alginate gel loaded with platelet-rich plasma to promote wound healing in rats

Wounds, especially chronic wounds, have become an important problem that endangers human health. At present, there are many repair methods, and among them combines materials science and biology is one of the important repair methods. This study explored the preparation method, physicochemical properties, biological activity and safety of Platelet-Rich plasma (PRP)-loaded slow-sculpting graphene oxide (GO)/alginate gel, and applied it to acute full-thickness skin defect wounds in rats to observe its role in wound healing. The results show that the slow-sculpting GO/alginate gel has excellent plasticity and is suitable for a variety of irregularly shaped wounds. At the same time, its porous structure and water content can maintain the activity of platelets and their released growth factors in PRP, thereby promoting wound collagen synthesis and angiogenesis to accelerate wound healing. This indicates that the slow-sculpting GO/alginate gel is an excellent loading material for PRP, and the combination of the two may become one of the methods to promote wound repair.

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34's deep representation learning and UNet's efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review

BACKGROUND

Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment.

OBJECTIVE

The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images.

METHODS

We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB).

RESULTS

In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB.

CONCLUSIONS

Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds

INTRODUCTION

The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML).

METHODS

Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy.

RESULTS

The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted.

CONCLUSIONS

These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

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.

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review

Significance: As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at providing an in-depth overview of the relevant methodologies of artificial intelligence (AI) and their potential implementation to support these growing needs of wound care and management. Recent Advances: MEDLINE, Compendex, Scopus, Web of Science, and IEEE databases were all searched for new AI methods or novel uses of existing AI methods for the diagnosis or management of hard-to-heal wounds. We only included English peer-reviewed original articles, conference proceedings, published patent applications, or granted patents (not older than 2010) where the performance of the utilized AI algorithms was reported. Based on these criteria, a total of 75 studies were eligible for inclusion. These varied by the type of the utilized AI methodology, the wound type, the medical record/database configuration, and the research goal. Critical Issues: AI methodologies appear to have a strong positive impact and prospects in the wound care and management arena. Another important development that emerged from the findings is AI-based remote consultation systems utilizing smartphones and tablets for data collection and connectivity. Future Directions: The implementation of machine-learning algorithms in the diagnosis and managements of hard-to-heal wounds is a promising approach for improving the wound care delivered to hospitalized patients, while allowing health care professionals to manage their working time more efficiently.

COMPUTER SYSTEMS TO AID IN WOUND HEALING: SCOPE REVIEW

Objective: To investigate studies that present computational systems to aid healing and systems which refer to the use of low-level laser.Method: Scope review that aimed to answer the question: Which computer systems help in wound healing? A subquestion was: Which of the computer systems refer to the use of low-level laser? Results: From the search, applying the eligibility criteria, 49 articles made up the final sample. The systems served multiple purposes in support of wound healing; the majority presented the health professional as a user of the system; medicine was the most mentioned professional area despite nursing being involved in the management of care for people with wounds. Innovation in care using the computer system was frequently reported, demonstrating the importance of this type of tool for clinical practice. There was a high frequency of the mobile platform, showing that this is a current trend. Conclusion:Computer systems have been used as tools to support patients and especially professionals in wound healing. Regarding the systems aimed at the low intensity laser, there was a shortage of computer systems for this purpose, with a study.

SISTEMAS COMPUTACIONAIS PARA AUXÍLIO NA CICATRIZAÇÃO DE FERIDAS: REVISÃO DE ESCOPO

Objetivo:Investigar estudos que apresentem sistemas computacionais de auxílio à cicatrização de feridas e quais sistemas se referem ao uso de laser de baixa intensidade. Método: Revisão de escopo que visou responder à questão de pesquisa: Quais sistemas computacionais auxiliam na cicatrização de feridas? Uma subquestão foi: quais sistemas computacionais se referem ao uso do laser de baixa intensidade? Resultados: A partir da busca, aplicando os critérios de elegibilidade, 49 artigos compuseram a amostra final. Os sistemas apresentaram várias finalidades de apoio à cicatrização de feridas, em que a maioria apresentou como usuário do sistema o profissional de saúde, sendo a medicina a área profissional mais mencionada, embora a enfermagem esteja envolvida com o manejo do cuidado às pessoas com feridas. Foi relatada com frequência a inovação na assistência a partir do uso do sistema computacional, o que demonstra a importância desse tipo de ferramenta para a prática clínica. Verificou-se com frequência o uso de plataforma mobile, como tendência da atualidade. Conclusão: Os sistemas computacionais têm sido utilizados como ferramentas para apoiar pacientes e principalmente profissionais na cicatrização de feridas. Quanto ao laser de baixa intensidade, houve escassez de sistemas computacionais com essa finalidade, com apenas um estudo.

Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review

Despite the advances in modern medicine, the use of data-driven technologies (DDTs) to prevent surgical site infections (SSIs) remains a major challenge. Scholars recognise that data management is the next frontier in infection prevention, but many aspects related to the benefits and advantages of using DDTs to mitigate SSI risk factors remain unclear and underexplored in the literature. This study explores how DDTs enable value creation in the prevention of SSIs. This study follows a systematic literature review approach and the PRISMA statement to analyse peer-reviewed articles from seven databases. Fifty-nine articles were included in the review and were analysed through a descriptive and a thematic analysis. The findings suggest a growing interest in DDTs in SSI prevention in the last 5 years, and that machine learning and smartphone applications are widely used in SSI prevention. DDTs are mainly applied to prevent SSIs in clean and clean-contaminated surgeries and often used to manage patient-related data in the postoperative stage. DDTs enable the creation of nine categories of value that are classified in four dimensions: cost/sacrifice, functional/instrumental, experiential/hedonic, and symbolic/expressive. This study offers a unique and systematic overview of the value creation aspects enabled by DDT applications in SSI prevention and suggests that additional research is needed in four areas: value co-creation and product-service systems, DDTs in contaminated and dirty surgeries, data legitimation and explainability, and data-driven interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41666-023-00129-2.

Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet

Goal: Augment a small, imbalanced, wound dataset by using semi-supervised learning with a secondary dataset. Then utilize the augmented wound dataset for deep learning-based wound assessment. Methods: The clinically-validated Photographic Wound Assessment Tool (PWAT) scores eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability to comprehensively assess chronic wound images. A small corpus of 1639 wound images labeled with ground truth PWAT scores was used as reference. A Semi-Supervised learning and Progressive Multi-Granularity training mechanism were used to leverage a secondary corpus of 9870 unlabeled wound images. Wound scoring utilized the EfficientNet Convolutional Neural Network on the augmented wound corpus. Results: Our proposed Semi-Supervised PMG EfficientNet (SS-PMG-EfficientNet) approach estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of about 90% on average, and outperformed a comprehensive list of baseline models and had a 7% improvement over the prior state-of-the-art (without data augmentation). We also demonstrate that synthetic wound image generation using Generative Adversarial Networks (GANs) did not improve wound assessment. Conclusions: Semi-supervised learning on unlabeled wound images in a secondary dataset achieved impressive performance for deep learning-based wound grading.

Fluorescence - modern method of the diagnosis of chronic wounds on the example of venous leg ulcer

Introduction

Fluorescence imaging has become a method for bacterial visualisation in chronic wounds for the last few years. MolecuLight i:X (MolecuLight, Inc, ON, Canada) is a handheld device, which enables quick diagnostics to determine both the type and location of pathogens present in the wound and on the skin. By means of fluorescent light illumination the tissues populated by pathogenic bacteria emit red or cyan fluorescent signatures, depending on the type of the pathogen: red fluorescence signal is emitted by Staphylococcus and Escherichia coli among others, while Pseudomonas aeruginosa produce cyan fluorescence. The fluorescence image also presents the spatial pattern of bacterial load, which creates bacterial mapping of the wound and may be used by a clinician for targeted sampling or debridement, among others.

Aim

This study presents the method of microbiological fluorescent imaging and two case studies of patients with venous leg ulcers.

Material and methods

In both cases, the sample for microbiological testing was obtained by means of a swab stick.

Results

The results obtained from fluorescent imaging showed moderate-to-heavy bacterial load, which corresponded with the results from microbiology laboratory. Thanks to quick diagnostics with the use of MolecuLight i:X device, instant implementation of targeted topical actions such as wound hygiene, skin disinfection, appropriate dressing choice and curative treatment among others was possible.

Conclusions

Our observations are consistent with the reports from other facilities.

Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models

Motivation: Infection (bacteria in the wound) and ischemia (insufficient blood supply) in Diabetic Foot Ulcers (DFUs) increase the risk of limb amputation. Goal: To develop an image-based DFU infection and ischemia detection system that uses deep learning. Methods: The DFU dataset was augmented using geometric and color image operations, after which binary infection and ischemia classification was done using the EfficientNet deep learning model and a comprehensive set of baselines. Results: The EfficientNets model achieved 99% accuracy in ischemia classification and 98% in infection classification, outperforming ResNet and Inception (87% accuracy) and Ensemble CNN, the prior state of the art (Classification accuracy of 90% for ischemia 73% for infection). EfficientNets also classified test images in a fraction (10% to 50%) of the time taken by baseline models. Conclusions: This work demonstrates that EfficientNets is a viable deep learning model for infection and ischemia classification.

Wound tissue segmentation by computerised image analysis of clinical pressure injury photographs: a pilot study

OBJECTIVE

Wound tissues can provide ample information about the wound development and healing process. However, the manual identification and measurement of wound tissue types is time-consuming and challenging due to the complexities of pressure injuries (PI). This study aims to develop an image analysis algorithm to automatically identify and differentiate wound tissue types from PI wound beds.

METHOD

This was a cross-sectional algorithm development study. PI photographs were obtained from a western Pennsylvania hospital. We used our previously developed wound bed segmentation tool to identify PI wound beds. We then used the k-means clustering method to classify the subzones on the wound beds. Finally, the support vector machine classifier was used to identify the classified subzones to certain types of wound tissue.

RESULTS

An image analysis algorithm was developed, using 64 selected PI photographs, to automatically identify different wound tissues for PIs.

CONCLUSION

Validation of the wound tissue identification of the PIs by image analysis algorithm demonstrated that our image analysis algorithm is a reliable and objective approach to monitoring wound healing progress through clinical PI photographs, and offers new insight into PI evaluation and documentation.

DECLARATION OF INTEREST

The authors have no conflicts of interest to declare.

Gamification for Machine Learning in Surgical Patient Engagement

Patients and their surgeons face a complex and evolving set of choices in the process of shared decision making. The plan of care must be tailored to individual patient risk factors and values, though objective estimates of risk can be elusive, and these risk factors are often modifiable and can alter the plan of care. Machine learning can perform real-time predictions of outcomes, though these technologies are limited by usability and interpretability. Gamification, or the use of game elements in non-game contexts, may be able to incorporate machine learning technology to help patients optimize their pre-operative risks, reduce in-hospital complications, and hasten recovery. This article proposes a theoretical mobile application to help guide decision making and provide evidence-based, tangible goals for patients and surgeons with the goal of achieving the best possible operative outcome that aligns with patient values.

Use of autologous conditioned serum dressings in hard-to-heal wounds: a randomised prospective clinical trial

OBJECTIVE

In this study, we aimed to assess both the efficacy and tolerability of autologous conditioned serum (ACS) as an innovative wound dressing in the local management of hard-to-heal wounds.

METHOD

In this single-blinded randomised controlled trial, patients with hard-to-heal wounds were randomly assigned to receive either ACS treatment or normal saline (NS) dressings. The treatment was applied once a week for three weeks with a final assessment at three weeks from the first ACS application.

RESULTS

A total of 30 patients took part in the study. Analysis of wound assessment data demonstrated statistically significant differences for wound surface area and Pressure Ulcer Scale for Healing scores (area score, exudate and tissue) from baseline to the end of the study in patients who received the ACS dressing, but not in patients who received the normal saline dressing. There were statistically significant differences in changes in: the wound surface area at week three (-6.4±2.69cm² versus +0.4±2.52cm²); area score at week three (-2.2±1.08 versus +0.2±0.86); exudate at week two (-1.2±0.70 versus +0.0±0.45) and at week 3 (-1.3±0.72 versus -0.1±0.63); tissue at week two (-1.1±0.35 versus +0.0±0.53) and at week three (-1.8±0.65 versus -0.1±0.63); and the PUSH total score at week one (-1.6±0.98 versus +0.4±1.22), week two (-3.2±0.86 versus +0.4±0.98) and week three (-5.3±1.17 versus -0.0±1.33) between the ACS and NS groups, respectively.

CONCLUSION

This trial revealed a significant decrease in wound surface area as well as a considerable improvement in wound healing in the ACS dressing group.

Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker

The number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to accelerate the application of treatments. For this reason, the infection probability index (IPI) is introduced as a novel infection marker based on thermal wound imaging. To improve usability, the IPI was implemented to automate scoring. Visual and thermal image pairs of 60 wounds were acquired to test the implemented algorithms on clinical data. The proposed process consists of (1) determining various parameters of the IPI based on medical hypotheses, (2) acquiring data, (3) extracting camera distortions using camera calibration, and (4) preprocessing and (5) automating segmentation of the wound to calculate (6) the IPI. Wound segmentation is reviewed by user input, whereas the segmented area can be refined manually. Furthermore, in addition to proof of concept, IPIs’ correlation with C-reactive protein (CRP) levels as a clinical infection marker was evaluated. Based on average CRP levels, the patients were clustered into two groups, on the basis of the separation value of an averaged CRP level of 100. We calculated the IPIs of the 60 wound images based on automated wound segmentation. Average runtime was less than a minute. In the group with lower average CRP, a correlation between IPI and CRP was evident.

Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds

BACKGROUND

The clinical characterization of the biological status of complex wounds remains a considerable challenge. Digital photography provides a non-invasive means of obtaining wound information and is currently employed to assess wounds qualitatively. Advances in machine learning (ML) image processing provide a means of identifying "hidden" features in pictures. This pilot study trains a convolutional neural network (CNN) to predict gene expression based on digital photographs of wounds in a canine model of volumetric muscle loss (VML).

MATERIALS AND METHODS

Images of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. A CNN was trained to regress gene expression values as a function of the extracted image segment (color and spatial distribution). Performance of the CNN was assessed in a held-back test set of images using Mean Absolute Percentage Error (MAPE).

RESULTS

The CNN was able to predict the gene expression of certain genes based on digital images, with a MAPE ranging from ∼10% to ∼30%, indicating the presence and identification of distinct, and identifiable patterns in gene expression throughout the wound.

CONCLUSIONS

These initial results suggest promise for further research regarding this novel use of ML regression on medical images. Specifically, the use of CNNs to determine the mechanistic biological state of a VML wound could aid both the design of future mechanistic interventions and the design of trials to test those therapies. Future work will expand the CNN training and/or test set, with potential expansion to predicting functional gene modules.

Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention

Goal: Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. Methods: We comprehensively score wounds based on the clinically-validated Photographic Wound Assessment Tool (PWAT), which comprehensively assesses clinically important ranges of eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability. We proposed a DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention to assess the 8 PWAT attributes of four wound types: diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds. Results: In an evaluation on our dataset of 1639 wound images, our model estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of over 80%. Conclusions: Our work is the first intelligent system that autonomously grades wounds comprehensively based on criteria in the PWAT rubric, alleviating the significant burden that manual wound grading imposes on wound care nurses.

Interplay between ESKAPE Pathogens and Immunity in Skin Infections: An Overview of the Major Determinants of Virulence and Antibiotic Resistance

The skin is the largest organ in the human body, acting as a physical and immunological barrier against pathogenic microorganisms. The cutaneous lesions constitute a gateway for microbial contamination that can lead to chronic wounds and other invasive infections. Chronic wounds are considered as serious public health problems due the related social, psychological and economic consequences. The group of bacteria known as ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter sp.) are among the most prevalent bacteria in cutaneous infections. These pathogens have a high level of incidence in hospital environments and several strains present phenotypes of multidrug resistance. In this review, we discuss some important aspects of skin immunology and the involvement of ESKAPE in wound infections. First, we introduce some fundamental aspects of skin physiology and immunology related to cutaneous infections. Following this, the major virulence factors involved in colonization and tissue damage are highlighted, as well as the most frequently detected antimicrobial resistance genes. ESKAPE pathogens express several virulence determinants that overcome the skin's physical and immunological barriers, enabling them to cause severe wound infections. The high ability these bacteria to acquire resistance is alarming, particularly in the hospital settings where immunocompromised individuals are exposed to these pathogens. Knowledge about the virulence and resistance markers of these species is important in order to develop new strategies to detect and treat their associated infections.

Bacterial chatter in chronic wound infections

One of the hallmark characteristics of chronic diabetic wounds is the presence of biofilm-forming bacteria. Bacteria encapsulated in a biofilm may coexist as a polymicrobial community and communicate with each other through a phenomenon termed quorum sensing (QS). Here, we describe the QS circuits of bacterial species commonly found in chronic diabetic wounds. QS relies on diffusion of signaling molecules and the local concentration changes of these molecules that bacteria experience in wounds. These biochemical signaling pathways play a role not only in biofilm formation and virulence but also in wound healing. They are, therefore, key to understanding the distinctive nature of these infections. While several in vivo and in vitro models exist to study QS in wounds, there has been limited progress in understanding the interplay between QS molecules and host factors that contribute to wound healing. Lastly, we examine the potential of targeting QS for both diagnosis and therapeutic intervention purposes.

Delivery of Therapeutics from Layer-by-Layer Electrospun Nanofiber Matrix for Wound Healing: An Update

Increasing incidences of chronic wounds urge the development of effective therapeutic wound treatment. As the conventional wound dressings are found not to comply with all the requirements of an ideal wound dressing, the development of alternative and effective dressings is demanded. Over the past few years, electrospun nanofiber has been recognized as a better system for wound dressing and hence has been studied extensively. Most of the electrospun nanofiber dressings were fabricated as single-layer structure mats. However, this design is less favorable for the effective healing of wounds mainly due to its burst release effect. To address this problem and to simulate the organized skin layer's structure and function, a multilayer structure of wound dressing had been proposed. This design enables a sustained release of the therapeutic agent(s), and more resembles the natural skin extracellular matrix. Multilayer structure is also referred to layer-by-layer (LbL), which has been established as an innovative method of drug incorporation and delivery, combines a high surface area of electrospun nanofibers with the multilayer structure mat. This review focuses on LbL multilayer electrospun nanofiber as a superior strategy in designing an optimal wound dressing.