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

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.

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.

Exploring Thermal Dynamics in Wound Healing: The Impact of Temperature and Microenvironment

Exploring the critical role of thermal dynamics in wound healing, this manuscript navigates through the complex biological responses initiated upon wound infliction and how temperature variations influence the healing trajectory. Integrating biothermal physics, clinical medicine, and biomedical engineering, it highlights the significance of thermal management in wound care, emphasizing the wound microenvironment's division into internal and external domains and their collaborative impact on tissue repair. Innovations in real-time wound temperature monitoring, especially through intelligent wireless sensor dressings, are spotlighted as transformative, enabling precise wound condition management. The text underscores the necessity for further research to elucidate thermal regulation's molecular and cellular mechanisms on healing processes. It advocates for standardized protocols for localized heating treatments, integrating them into personalized wound care strategies to enhance therapeutic outcomes, improve patient well-being, and achieve cost-effective healthcare practices. This work presents a forward-looking perspective on refining wound management through sophisticated, evidence-based interventions, emphasizing the interplay between thermal dynamics and wound healing.

Application of deep learning to pressure injury staging

OBJECTIVE

Accurate assessment of pressure injuries (PIs) is necessary for a good outcome. Junior and non-specialist nurses have less experience with PIs and lack clinical practice, and so have difficulty staging them accurately. In this work, a deep learning-based system for PI staging and tissue classification is proposed to help improve its accuracy and efficiency in clinical practice, and save healthcare costs.

METHOD

A total of 1610 cases of PI and their corresponding photographs were collected from clinical practice, and each sample was accurately staged and the tissues labelled by experts for training a Mask Region-based Convolutional Neural Network (Mask R-CNN, Facebook Artificial Intelligence Research, Meta, US) object detection and instance segmentation network. A recognition system was set up to automatically stage and classify the tissues of the remotely uploaded PI photographs.

RESULTS

On a test set of 100 samples, the average precision of this model for stage recognition reached 0.603, which exceeded that of the medical personnel involved in the comparative evaluation, including an enterostomal therapist.

CONCLUSION

In this study, the deep learning-based PI staging system achieved the evaluation performance of a nurse with professional training in wound care. This low-cost system could help overcome the difficulty of identifying PIs by junior and non-specialist nurses, and provide valuable auxiliary clinical information.

Updated Scenario on Negative Pressure Wound Therapy

Negative pressure wound therapy (NPWT) is a widely used and effective treatment for managing complex wounds. This document discusses how NPWT can be used in wound care in an updated way. The updated scenario on NPWT provides a concise overview of the current state of NPWT and its implications in clinical practice. It highlights recent developments in NPWT, as well as the advancements in this field. As part of NPWT, vacuum-assisted closure is used and negative pressure is applied to the wound bed. It discusses the key components and mechanisms. In addition to improving wound healing, NPWT also reduces infection rates and improves patient comfort, among other benefits. In addition, this document discusses the specific indications and contraindications of NPWT, as well as the types of wounds that can be treated with NPWT, including diabetic foot ulcers, pressure ulcers, and traumatic wounds. The document emphasizes the importance of choosing patients appropriately and assessing wounds to ensure optimal outcomes. In addition, it provides evidence-based guidelines and clinical recommendations on NPWT. In addition to reviewing the latest research findings supporting NPWT in a variety of clinical settings, it also discusses randomized controlled trials and systematic reviews. In addition, it discusses the potential complications and challenges associated with NPWT, including pain, bleeding, and device malfunction. The purpose of this document is to shed light on the role of NPWT in wound care management by providing an updated scenario. NPWT can be incorporated into clinical practice by healthcare professionals if they understand its principles, benefits, indications, and limitations. Healthcare providers can optimize patient outcomes and improve wound healing in diverse patient populations by staying abreast of the latest advancements in NPWT.

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.

Hydrogel-enabled mechanically active wound dressings

Wound care is a major clinical and social concern. However, effective wound repair remains challenging where conventional dressings yield detrimental healing outcomes. An emerging technique, named mechanically active dressing (MAD), uses self-contractile hydrogels to mechanically contract the wound bed. MAD has shown improved healing rates with limited side effects. These promising developments in wound care call for a timely review on the development of such technology. Herein, we shed light on the mechanism underlying mechanically modulated wound healing, carry out a systematic discussion on the status quo of designing hydrogels for MAD fabrication, and conclude with perspectives on design, use and clinical translation for realizing the future goal of personalized wound care.

Point-of-care detection devices for wound care and monitoring

Healthcare resources are heavily burdened by infections that impede the wound-healing process. A wide range of advanced technologies have been developed for detecting and quantifying infection biomarkers. Finding a timely, accurate, non-invasive diagnostic alternative that does not require a high level of training is a critical step toward arresting common clinical patterns of wound health decline. There is growing interest in the development of innovative diagnostics utilizing a variety of emerging technologies, and new biomarkers have been investigated as potential indicators of wound infection. In this review, we summarize diagnostics available for wound infection, including those used in clinics and still under development.

Validating Wound Severity Assessment via Region-Anchored Convolutional Neural Network Model for Mobile Image-Based Size and Tissue Classification

Evaluating and tracking the size of a wound is a crucial step in wound assessment. The measurement of various indicators on wounds over time plays a vital role in treating and managing crucial wounds. This article introduces the concept of utilizing mobile device-captured photographs to address this challenge. The research explores the application of digital technologies in the treatment of chronic wounds, offering tools to assist healthcare professionals in enhancing patient care and decision-making. Additionally, it investigates the use of deep learning (DL) algorithms along with the use of computer vision techniques to enhance the validation results of wounds. The proposed method involves tissue classification as well as visual recognition system. The wound's region of interest (RoI) is determined using superpixel techniques, enabling the calculation of its wounded zone. A classification model based on the Region Anchored CNN framework is employed to detect and differentiate wounds and classify their tissues. The outcome demonstrates that the suggested method of DL, with visual methodologies to detect the shape of a wound and measure its size, achieves exceptional results. By utilizing Resnet50, an accuracy of 0.85 percent is obtained, while the Tissue Classification CNN exhibits a Median Deviation Error of 2.91 and a precision range of 0.96%. These outcomes highlight the effectiveness of the methodology in real-world scenarios and its potential to enhance therapeutic treatments for patients with chronic wounds.

The Efficiency and Safety of Platelet-Rich Plasma Dressing in the Treatment of Chronic Wounds: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Recently, many clinical trials have applied platelet-rich plasma (PRP) dressings to treat wounds that have stopped healing, which are also called chronic wounds. However, the clinical efficiency of PRP dressings in treating chronic wounds is still controversial. Therefore, we conducted this study to compare PRP dressings with normal saline dressings in treating chronic wounds. Relevant randomized controlled trials focusing on utilizing PRP dressings in treating chronic wounds were extracted from bibliographic databases. Finally, 330 patients with chronic wounds, reported in eight randomized controlled trials, were included in this study. In total, 169 out of 330 (51.21%) were treated with PRP dressings, and 161 out of 330 (48.79%) were treated with normal saline dressings. The pooled results showed that the complete healing rate of the PRP group was significantly higher than that of saline group at 8 weeks and 12 weeks, respectively. In addition, there were no significant differences in wound infection and adverse events. Compared with normal saline dressing, the PRP dressing could effectively enhance the prognosis of chronic wounds. Furthermore, the PRP did not increase wound infection rate or occurrence of adverse events as an available treatment for chronic wounds.