Monitoring Wound Healing With Contactless Measurements and Augmented Reality

Large Scale Ultrafast Manufacturing of Wireless Soft Bioelectronics Enabled by Autonomous Robot Arm Printing Assisted by a Computer Vision-Enabled Guidance System for Personalized Wound Healing

A Customized wound patch for Advanced tissue Regeneration with Electric field (CARE), featuring an autonomous robot arm printing system guided by a computer vision-enabled guidance system for fast image recognition is introduced. CARE addresses the growing demand for flexible, stretchable, and wireless adhesive bioelectronics tailored for electrotherapy, which is suitable for rapid adaptation to individual patients and practical implementation in a comfortable design. The visual guidance system integrating a 6-axis robot arm enables scans from multiple angles to provide a 3D map of complex and curved wounds. The size of electrodes and the geometries of power-receiving coil are essential components of the CARE and are determined by a MATLAB simulation, ensuring efficient wireless power transfer. Three heterogeneous inks possessing different rheological behaviors can be extruded and printed sequentially on the flexible substrates, supporting fast manufacturing of large customized bioelectronic patches. CARE can stimulate wounds up to 10 mm in depth with an electric field strength of 88.8 mV mm-1. In vitro studies reveal the ability to accelerate cell migration by a factor of 1.6 and 1.9 for human dermal fibroblasts and human umbilical vein endothelial cells, respectively. This study highlights the potential of CARE as a clinical wound therapy method to accelerate healing.

Recent technological advancements in Artificial Intelligence for orthopaedic wound management

In orthopaedics, wound care is crucial as surgical site infections carry disease burden due to increased length of stay, decreased quality of life and poorer patient outcomes. Artificial Intelligence (AI) has a vital role in revolutionising wound care in orthopaedics: ranging from wound assessment, early detection of complications, risk stratifying patients, and remote patient monitoring. Incorporating AI in orthopaedics has reduced dependency on manual physician assessment which is time-consuming. This article summarises current literature on how AI is used for wound assessment and management in the orthopaedic community.

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.

Semiautonomous Robotic Manipulator for Minimally Invasive Aortic Valve Replacement

Aortic valve surgery is the preferred procedure for replacing a damaged valve with an artificial one. The ValveTech robotic platform comprises a flexible articulated manipulator and surgical interface supporting the effective delivery of an artificial valve by teleoperation and endoscopic vision. This article presents our recent work on force-perceptive, safe, semiautonomous navigation of the ValveTech platform prior to valve implantation. First, we present a force observer that transfers forces from the manipulator body and tip to a haptic interface. Second, we demonstrate how hybrid forward/inverse mechanics, together with endoscopic visual servoing, lead to autonomous valve positioning. Benchtop experiments and an artificial phantom quantify the performance of the developed robot controller and navigator. Valves can be autonomously delivered with a 2.0±0.5 mm position error and a minimal misalignment of 3.4±0.9°. The hybrid force/shape observer (FSO) algorithm was able to predict distributed external forces on the articulated manipulator body with an average error of 0.09 N. FSO can also estimate loads on the tip with an average accuracy of 3.3%. The presented system can lead to better patient care, delivery outcome, and surgeon comfort during aortic valve surgery, without requiring sensorization of the robot tip, and therefore obviating miniaturization constraints.

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.

Full-thickness skin columns: A method to reduce healing time and donor site morbidity in deep partial-thickness burns

The current standard of care for the coverage of large wounds often involves split thickness skin grafts (STSGs) which have numerous limitations. One promising technique that has gained traction is fractional autologous skin grafting using full-thickness skin columns (FTSC). Harvesting occurs orthogonally by taking numerous individual skin columns containing the epidermis down through the dermis and transferring them to the wound bed. The purpose of this porcine study was to investigate the efficacy of implanting FTSCs directly into deep partial-thickness burn wounds, as well as examining donor site healing at the maximal harvest density. It was hypothesised that by utilising FTSCs, the rate of healing in deep partial thickness burns can be improved without incurring the donor morbidity seen in other methods of skin grafting. Deep partial-thickness burns were created on the dorsum of female red duroc swine, debrided 3 days later and FTSCs were implanted at varying expansion ratios directly into the burn wounds. At day 14, 1:50 expansion ratio showed significantly faster re-epithelialisation compared to the debrided burn control and 1:200. Donor sites (at 7%-10% harvest density) were 100% re-epithelialised by day 7. Additionally, the maximal harvest density was determined to be 28% in an ex vivo model, which then five donor sites were harvested at 28% density on a red duroc swine and compared to five STSG donor sites. At maximal harvest density, FTSC donor sites were significantly less hypopigmented compared to STSGs, but no significant differences were observed in re-epithelialisation, contraction, blood flow or dermal thickness. In conclusion, implantation directly into deep partial-thickness burns is a viable option for the application of FTSCs, favouring lower expansion ratios like 1:50 or lower. Little difference in donor site morbidity was observed between FTSC at a maximal harvest density of 28% and STSGs, exceeding the optimal harvest density.

The Role of Immersive Virtual Reality and Augmented Reality in Medical Communication: A Scoping Review

Communication between clinicians and patients and communication within clinical teams is widely recognized as a tool through which improved patient outcomes can be achieved. As emerging technologies, there is a notable lack of commentary on the role of immersive virtual reality (VR) and augmented reality (AR) in enhancing medical communication. This scoping review aims to map the current landscape of literature on this topic and highlights gaps in the evidence to inform future endeavors. A comprehensive search strategy was conducted across 3 databases (PubMed, Web of Science, and Embase), yielding 1000 articles, of which 623 were individually screened for relevance. Ultimately, 22 articles were selected for inclusion and review. Similarities across the cohort of studies included small sample sizes, observational study design, use of questionnaires, and more VR studies than AR. The majority of studies found these technologies to improve medical communication, although user tolerability limitations were identified. More studies are required, presenting more robust findings, in order to draw more definitive conclusions and stronger recommendations for use of immersive VR/AR in clinical environments.

Head-Mounted Projector for Manual Precision Tasks: Performance Assessment

The growing interest in augmented reality applications has led to an in-depth look at the performance of head-mounted displays and their testing in numerous domains. Other devices for augmenting the real world with virtual information are presented less frequently and usually focus on the description of the device rather than on its performance analysis. This is the case of projected augmented reality, which, compared to head-worn AR displays, offers the advantages of being simultaneously accessible by multiple users whilst preserving user awareness of the environment and feeling of immersion. This work provides a general evaluation of a custom-made head-mounted projector for the aid of precision manual tasks through an experimental protocol designed for investigating spatial and temporal registration and their combination. The results of the tests show that the accuracy (0.6±0.1 mm of spatial registration error) and motion-to-photon latency (113±12 ms) make the proposed solution suitable for guiding precision tasks.

Advances in non-invasive biosensing measures to monitor wound healing progression

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.

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.

Evaluation of a Wearable AR Platform for Guiding Complex Craniotomies in Neurosurgery

Today, neuronavigation is widely used in daily clinical routine to perform safe and efficient surgery. Augmented reality (AR) interfaces can provide anatomical models and preoperative planning contextually blended with the real surgical scenario, overcoming the limitations of traditional neuronavigators. This study aims to demonstrate the reliability of a new-concept AR headset in navigating complex craniotomies. Moreover, we aim to prove the efficacy of a patient-specific template-based methodology for fast, non-invasive, and fully automatic planning-to-patient registration. The AR platform navigation performance was assessed with an in-vitro study whose goal was twofold: to measure the real-to-virtual 3D target visualization error (TVE), and assess the navigation accuracy through a user study involving 10 subjects in tracing a complex craniotomy. The feasibility of the template-based registration was preliminarily tested on a volunteer. The TVE mean and standard deviation were 1.3 and 0.6 mm. The results of the user study, over 30 traced craniotomies, showed that 97% of the trajectory length was traced within an error margin of 1.5 mm, and 92% within a margin of 1 mm. The in-vivo test confirmed the feasibility and reliability of the patient-specific template for registration. The proposed AR headset allows ergonomic and intuitive fruition of preoperative planning, and it can represent a valid option to support neurosurgical tasks.

Functional Imaging in Wounds: Imaging Modalities of Today and Tomorrow

Wound care is a multidisciplinary field with significant economic burden to our healthcare system. Not only does wound care cost the US healthcare system $20 billion annually, but wounds also remarkably impact the quality of life of patients; wounds pose significant risk of mortality, as the five-year mortality rate for diabetic foot ulcers (DFUs) and ischemic ulcers is notably higher compared to commonly encountered cancers such as breast and prostate. Although it is important to measure how wounds may or may not be improving over time, the only relative "marker" for this is wound area measurement-area measurements can help providers determine if a wound is on a healing or non-healing trajectory. Because wound area measurements are currently the only readily available "gold standard" for predicting healing outcomes, there is a pressing need to understand how other relative biomarkers may play a role in wound healing. Currently, wound care centers across the nation employ various techniques to obtain wound area measurements; length and width of a wound can be measured with a ruler, but this carries a high amount of inter- and intrapersonal error as well as uncertainty. Acetate tracings could be used to limit the amount of error but do not account for depth, thereby making them inaccurate. Here, we discuss current imaging modalities and how they can serve to accurately measure wound size and serve as useful adjuncts in wound assessment. Moreover, new imaging modalities are also discussed and how up-and-coming technologies can provide important information on "biomarkers" for wound healing.

SfM-3DULC: Reliability of a new 3D wound measurement procedure and its accuracy in projected area

Three‐dimensional (3D) wound measurement lacks a gold standard to test accuracy. It is useful to develop procedures to scan wounds and reconstruct their 3D model with low‐cost techniques. We present a new procedure (Structure from Motion [SfM]‐3DULC) that uses photographs for measuring nine wound variables. We also propose a new variant of ImageJ in which an orthophoto is used to measure the projected area (Ortho‐ImageJ). In addition, we compare the wound measurements made by dermatologists and non‐experts. A group of five experts in dermatology and five non‐specialists measured 33 leg wounds five times per procedure. Intra‐rater and inter‐rater reliability scores of SfM‐3DULC were evaluated with the intraclass correlation coefficient (ICC 2,1). The accuracy of the two new procedures (SfM‐3DULC and Ortho‐ImageJ) in the measurement of projected area was assessed by comparing their values with those obtained using ImageJ, with the Wilcoxon matched‐pairs signed rank test (α = 0.05). This test was also used to analyse the differences between the measurements made by dermatologists and non‐experts. All the variables measured by dermatologists using SfM‐3DULC showed excellent scores of intra‐rater reliability (ICC > 0.99) and inter‐rater reliability (ICC > 0.98). No significant differences between the three procedures were found when comparing their projected area values. Significant differences between the measurements of dermatologists and non‐experts were found in most of the variables: circularity coefficient, perimeter, projected area, surface area, and reference surface area. The wound measurement procedure SfM‐3DULC has an excellent reliability, is accurate for the measurement of projected area, and can be used by dermatologists for wound monitoring in everyday clinical practice.

Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools

IMPORTANCE

Accurate assessment of wound area and percentage of granulation tissue (PGT) are important for optimizing wound care and healing outcomes. Artificial intelligence (AI)-based wound assessment tools have the potential to improve the accuracy and consistency of wound area and PGT measurement, while improving efficiency of wound care workflows.

OBJECTIVE

To develop a quantitative and qualitative method to evaluate AI-based wound assessment tools compared with expert human assessments.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was performed across 2 independent wound centers using deidentified wound photographs collected for routine care (site 1, 110 photographs taken between May 1 and 31, 2018; site 2, 89 photographs taken between January 1 and December 31, 2019). Digital wound photographs of patients were selected chronologically from the electronic medical records from the general population of patients visiting the wound centers. For inclusion in the study, the complete wound edge and a ruler were required to be visible; circumferential ulcers were specifically excluded. Four wound specialists (2 per site) and an AI-based wound assessment service independently traced wound area and granulation tissue.

MAIN OUTCOMES AND MEASURES

The quantitative performance of AI tracings was evaluated by statistically comparing error measure distributions between test AI traces and reference human traces (AI vs human) with error distributions between independent traces by 2 humans (human vs human). Quantitative outcomes included statistically significant differences in error measures of false-negative area (FNA), false-positive area (FPA), and absolute relative error (ARE) between AI vs human and human vs human comparisons of wound area and granulation tissue tracings. Six masked attending physician reviewers (3 per site) viewed randomized area tracings for AI and human annotators and qualitatively assessed them. Qualitative outcomes included statistically significant difference in the absolute difference between AI-based PGT measurements and mean reviewer visual PGT estimates compared with PGT estimate variability measures (ie, range, standard deviation) across reviewers.

RESULTS

A total of 199 photographs were selected for the study across both sites; mean (SD) patient age was 64 (18) years (range, 17-95 years) and 127 (63.8%) were women. The comparisons of AI vs human with human vs human for FPA and ARE were not statistically significant. AI vs human FNA was slightly elevated compared with human vs human FNA (median [IQR], 7.7% [2.7%-21.2%] vs 5.7% [1.6%-14.9%]; P < .001), indicating that AI traces tended to slightly underestimate the human reference wound boundaries compared with human test traces. Two of 6 reviewers had a statistically higher frequency in agreement that human tracings met the standard area definition, but overall agreement was moderate (352 yes responses of 583 total responses [60.4%] for AI and 793 yes responses of 1166 total responses [68.0%] for human tracings). AI PGT measurements fell in the typical range of variation in interreviewer visual PGT estimates; however, visual PGT estimates varied considerably (mean range, 34.8%; mean SD, 19.6%).

CONCLUSIONS AND RELEVANCE

This study provides a framework for evaluating AI-based digital wound assessment tools that can be extended to automated measurements of other wound features or adapted to evaluate other AI-based digital image diagnostic tools. As AI-based wound assessment tools become more common across wound care settings, it will be important to rigorously validate their performance in helping clinicians obtain accurate wound assessments to guide clinical care.