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

Clinical, Operational, and Economic Benefits of a Digitally Enabled Wound Care Program in Home Health: Quasi-Experimental, Pre-Post Comparative Study

BACKGROUND

The demand for home health care and nursing visits has steadily increased, requiring significant allocation of resources for wound care. Many home health agencies operate below capacity due to clinician shortages, meeting only 61% to 70% of demand and frequently declining wound care referrals. Implementing artificial intelligence-powered digital wound care solutions (DWCSs) offers an opportunity to enhance wound care programs by improving scalability and effectiveness through better monitoring and risk identification.

OBJECTIVE

This study assessed clinical and operational outcomes across 14 home health branches that adopted a DWCS, comparing pre- and postadoption data and outcomes with 27 control branches without the technology.

METHODS

This pre-post comparative study analyzed clinical outcomes, including average days to wound healing, and operational outcomes, such as skilled nursing (SN) visits per episode (VPE) and in-home visit durations, during two 7-month intervals (from November to May in 2020-2021 and 2021-2022). Data were extracted from 14,278 patients who received wound care across adoption and control branches. Projected cost savings were also calculated based on reductions in SN visits.

RESULTS

The adoption branches showed a 4.3% reduction in SN VPE and a 2.5% reduction in visit duration, saving approximately 309 staff days. In contrast, control branches experienced a 4.5% increase in SN VPE and a 2.2% rise in visit duration, adding 42 days. Healing times improved significantly in the adoption branches, with a reduction of 4.3 days on average per wound compared to 1.6 days in control branches (P<.001); pressure injuries, venous ulcers, and surgical wounds showed the most substantial improvements.

CONCLUSIONS

Integrating digital wound management technology enhances clinical outcomes, operational efficiencies, and cost savings in home health settings. A reduction of 0.3 SN VPE could generate annual savings of up to US $958,201 across the organization. The adoption branches avoided 1187 additional visits during the study period. If control branches had implemented the DWCS and achieved similar outcomes, they would have saved 18,546 healing days. These findings emphasize the importance of incorporating DWCSs into wound care programs to address increasing demands, clinician shortages, and rising health care costs while maintaining positive clinical outcomes.

Optimization and advances in negative pressure wound therapy for the management of necrotizing fasciitis in the upper limb

Necrotizing fasciitis (NF) is a rapidly progressing, life-threatening soft tissue infection, with upper limb NF posing a particularly serious threat to patient survival and quality of life. Negative pressure wound therapy (NPWT) has shown considerable advantages in accelerating wound healing and mitigating functional impairment. A retrospective study by Lipatov et al. demonstrated that NPWT significantly reduced the time needed for wound closure preparation while enhancing the success rate of local repair. Despite its benefits, certain limitations highlight the need for further optimization. This paper investigates the potential for personalized dynamic regulation of NPWT, its integration with adjunctive therapies, and the role of multidisciplinary collaboration. Furthermore, it explores the incorporation of advanced technologies such as artificial intelligence, imaging modalities, and biomaterials, presenting novel pathways for the personalized management and global standardization of NF treatment.

Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review

Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively.

Development of a Deep Learning-Based Model for Pressure Injury Surface Assessment

AIM

To develop a deep learning-based smart assessment model for pressure injury surface.

DESIGN

Exploratory analysis study.

METHODS

Pressure injury images from four Guangzhou hospitals were labelled and used to train a neural network model. Evaluation metrics included mean intersection over union (MIoU), pixel accuracy (PA), and accuracy. Model performance was tested by comparing wound number, maximum dimensions and area extent.

RESULTS

From 1063 images, the model achieved 74% IoU, 88% PA and 83% accuracy for wound bed segmentation. Cohen's kappa coefficient for wound number was 0.810. Correlation coefficients were 0.900 for maximum length (mean difference 0.068 cm), 0.814 for maximum width (mean difference 0.108 cm) and 0.930 for regional extent (mean difference 0.527 cm2).

CONCLUSION

The model demonstrated exceptional automated estimation capabilities, potentially serving as a crucial tool for informed decision-making in wound assessment.

IMPLICATIONS AND IMPACT

This study promotes precision nursing and equitable resource use. The AI-based assessment model serves clinical work by assisting healthcare professionals in decision-making and facilitating wound assessment resource sharing.

REPORTING METHOD

The STROBE checklist guided study reporting.

PATIENT OR PUBLIC CONTRIBUTION

Patients provided image resources for model training.

Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics

Skin injuries caused by physical, pathological, and chemical factors not only compromise appearance and barrier function but can also lead to life-threatening microbial infections, posing significant challenges for patients and healthcare systems. Artificial intelligence (AI) technology has demonstrated substantial advantages in processing and analyzing image information. Recently, AI-based methods and algorithms, including machine learning, deep learning, and neural networks, have been extensively explored in wound care and research, providing effective clinical decision support for wound diagnosis, treatment, prognosis, and rehabilitation. However, challenges remain in achieving a closed-loop care system for the comprehensive application of AI in wound management, encompassing wound diagnosis, monitoring, and treatment. This review comprehensively summarizes recent advancements in AI applications in wound repair. Specifically, it discusses AI's role in injury type classification, wound measurement (including area and depth), wound tissue type classification, wound monitoring and prediction, and personalized treatment. Additionally, the review addresses the challenges and limitations AI faces in wound management. Finally, recommendations for the application of AI in wound repair are proposed, along with an outlook on future research directions, aiming to provide scientific evidence and technological support for further advancements in AI-driven wound repair theranostics.

Non-invasive techniques for wound assessment: A comprehensive review

Efficient wound assessment is essential for healthcare teams to facilitate prompt diagnosis, optimize treatment plans, reduce workload, and enhance patients' quality of life. In recent years, non-invasive techniques for aiding wound assessment, such as digital photography, 3D modelling, optical imaging, fluorescence and thermography, as well as artificial intelligence, have been gradually developed. This paper aims to review the various methods of measurement and diagnosis based on non-invasive wound imaging, and to summarize their application in wound monitoring and assessment. The goal is to provide a foundation and reference for future research on wound assessment.

Co-creation and evaluation of an algorithm for the development of a mobile application for wound care among new graduate nurses: A mixed methods study

Chronic wounds are a growing concern due to aging populations, sedentary lifestyles and increasing rates of obesity and chronic diseases. The impact of such wounds is felt worldwide, posing a considerable clinical, environmental and socioeconomic challenge and impacting the quality of life. The increasing complexity of care requires a holistic approach, along with extensive knowledge and skills. The challenge experienced by health-care professionals is particularly significant for newly graduate nurses, who face a gap between theory and practice. Digital tools, such as mobile applications, can support wound care by facilitating more precise assessments, early treatment, complication prevention and better outcomes. They also aid in clinical decision-making and improve healthcare delivery in remote areas. Several mobile applications have emerged to enhance wound care. However, there are no applications dedicated to newly graduate nurses. The aim of this study was to co-create and evaluate an algorithm for the development of a wound care mobile application supporting clinical decisions for new graduate nurses. The development of this mobile application is envisioned to improve knowledge application and facilitate evidence-based practice. This study is part of a multiphase project that adopted a pragmatic epistemological approach, using the 'Knowledge-to-Action' conceptual model and Duchscher's Stages of Transition Theory. Following a scoping review, an expert consensus, and stakeholder meetings, this study was pursued through a sequential exploratory mixed methods design carried out in two phases. In the initial phase, 21 participants engaged in semi-structured focus groups to explore their needs regarding clinical decision support in wound care, explore their perceptions of the future mobile application's content and identify and categorize essential components. Through descriptive analysis, five overarching themes emerged, serving as guiding principles for conceptual data model development and refinement. These findings confirmed the significance of integrating a comprehensive glossary complemented by photos, ensuring compatibility between the mobile application and existing documentation systems, and providing quick access to information to avoid burdening work routines. Subsequently, the algorithm was created from the qualitative data collected. The second phase involved presenting an online SurveyMonkey® questionnaire to 34 participants who were not part of the initial phase to quantitatively measure the usability of this algorithm among future users. This phase revealed very positive feedback regarding the usability [score of 6.33 (±0.19) on a scale of 1-7], which reinforces its quality. The technology maturation process can now continue with the development of a prototype and subsequent validation in a laboratory setting.

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.

Exploring Synergistic Effects of Bioprinted Extracellular Vesicles for Skin Regeneration

Regenerative medicine represents a paradigm shift in healthcare, aiming to restore tissue and organ function through innovative therapeutic strategies. Among these, bioprinting and extracellular vesicles (EVs) have emerged as promising techniques for tissue rejuvenation. EVs are small lipid membrane particles secreted by cells, known for their role as potent mediators of intercellular communication through the exchange of proteins, genetic material, and other biological components. The integration of 3D bioprinting technology with EVs offers a novel approach to tissue engineering, enabling the precise deposition of EV-loaded bioinks to construct complex three-dimensional (3D) tissue architectures. Unlike traditional cell-based approaches, bioprinted EVs eliminate the need for live cells, thereby mitigating regulatory and financial obstacles associated with cell therapy. By leveraging the synergistic effects of EVs and bioprinting, researchers aim to enhance the therapeutic outcomes of skin regeneration while addressing current limitations in conventional treatments. This review explores the evolving landscape of bioprinted EVs as a transformative approach for skin regeneration. Furthermore, it discusses the challenges and future directions in harnessing this innovative therapy for clinical applications, emphasizing the need for interdisciplinary collaboration and continued scientific inquiry to unlock its full therapeutic potential.

Colorimetric Analyses of the Optic Nerve Head and Retina Indicate Increased Blood Flow After Vitrectomy

Purpose

The purpose of this study was to evaluate the impact of vitrectomy and posterior hyaloid (PH) peeling on color alteration of optic nerve head (ONH) and retina as a surrogate biomarker of induced perfusion changes.

Methods

Masked morphometric and colorimetric analyses were conducted on preoperative (<1 month) and postoperative (<18 months) color fundus photographs of 54 patients undergoing vitrectomy, either with (44) or without (10) PH peeling and 31 years of age and gender-matched control eyes. Images were calibrated according to the hue and saturation values of the parapapillary venous blood column. Chromatic spectra of the retinal pigment epithelium and choroid were subtracted to avoid color aberrations. Red, green, and blue (RGB) bit values over the ONH and retina were plotted within the constructed RGB color space to analyze vitrectomy-induced color shift. Vitrectomy-induced parapapillary vein caliber changes were also computed morphometrically.

Results

A significant post-vitrectomy red hue shift was noted on the ONH (37.1 degrees ± 10.9 degrees vs. 4.1 degrees ± 17.7 degrees, P < 0.001), which indicates a 2.8-fold increase in blood perfusion compared to control (2.6 ± 1.9 vs. 0.9 ± 1.8, P < 0.001). A significant post-vitrectomy increase in the retinal vein diameter was also noticed (6.8 ± 6.4% vs. 0.1 ± 0.3%, P < 0.001), which was more pronounced with PH peeling (7.9 ± 6.6% vs. 3.1 ± 4.2%, P = 0.002).

Conclusions

Vitrectomy and PH peeling increase ONH and retinal blood flow. Colorimetric and morphometric analyses offer valuable insights for future artificial intelligence and deep learning applications in this field.

Translational Relevance

The methodology described herein can easily be applied in different clinical settings and may enlighten the beneficial effects of vitrectomy in several retinal vascular diseases.

Integrating technology into a successful apomorphine delivery program in Thailand: a 10-year journey of achievements with a five-motto concept

Introduction

Apomorphine, a potent dopamine agonist, is a therapeutic option for patients with Parkinson's disease and motor fluctuations. However, the adoption of and adherence to this therapy have been limited by the need for complex delivery devices and specialized care as well as resource consumption, posing challenges for new physicians. Thailand is a unique example of a developing nation that has successfully implemented and continued the use of this therapy by employing cooperative technology that has dramatically enhanced apomorphine delivery services.

Methods

Establishing apomorphine delivery services requires significant resources and step-by-step solutions. We began our services by implementing various strategies in three chronological stages: the initial stage (2013-2015), intermediate stage (2016-2019), and current stage (2020-present), each presenting unique challenges. Together, we also implemented a proposed set of five mottos to strengthen our apomorphine delivery service. Using additive technology, we developed a patient registry platform that combined electronic data acquisition, video and remote monitoring using wearable sensors, and in-house mobile applications to support our service.

Results

At the initial stage, we assembled a team to enhance the efficacy and confirm the safety of apomorphine treatment in our hospital. At the intermediate stage, we expanded our apomorphine delivery services beyond just the patients at our hospital. We supported other hospitals in Thailand in setting up their own apomorphine services by educating both physicians and nurses regarding apomorphine therapy. With this educational undertaking, increased apomorphine-related knowledge among medical professionals, and a greater number of hospitals providing apomorphine services, an increasing number of patients were administered apomorphine in subsequent years. Currently, we are providing effective apomorphine delivery to improve patient outcomes and are seamlessly integrating technology into clinical practice. Incorporating integrative technologies in our apomorphine delivery program yielded positive results in data collection and support throughout patient care, in tracking patients' statuses, in the long-term use of this treatment, and in increasing medication adherence rates.

Conclusion

This perspective paper describes how technology can help provide supportive healthcare services in resource-constrained environments, such as in Thailand, offering a step-by-step approach to overcoming several limitations. The valuable insights from our 10-year journey in successfully integrating technology into apomorphine delivery services can benefit new physicians seeking to replicate our success.

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.

Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning Techniques and Applications

Ten percent of adults in the United States have a diagnosis of diabetes and up to a third of these individuals will develop a diabetic foot ulcer (DFU) in their lifetime. Of those who develop a DFU, a fifth will ultimately require amputation with a mortality rate of up to 70% within five years. The human suffering, economic burden, and disproportionate impact of diabetes on communities of color has led to increasing interest in the use of computer vision (CV) and machine learning (ML) techniques to aid the detection, characterization, monitoring, and even prediction of DFUs. Remote monitoring and automated classification are expected to revolutionize wound care by allowing patients to self-monitor their wound pathology, assist in the remote triaging of patients by clinicians, and allow for more immediate interventions when necessary. This scoping review provides an overview of applicable CV and ML techniques. This includes automated CV methods developed for remote assessment of wound photographs, as well as predictive ML algorithms that leverage heterogeneous data streams. We discuss the benefits of such applications and the role they may play in diabetic foot care moving forward. We highlight both the need for, and possibilities of, computational sensing systems to improve diabetic foot care and bring greater knowledge to patients in need.