Artificial Intelligence in Wound Care Education: Protocol for a Scoping Review

Encarnação, Rúben; Manuel, Tânia; Palheira, Hélder; Neves-Amado, João; Alves, Paulo

doi:10.3390/nursrep14010048

Cited by 4 publications

References 42 publications

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Wound area severity index (WASI): A novel tool for assessing and predicting healing times in hidradenitis suppurativa postsurgical wounds

Michelucci,

Manzo Margiotta,

Innocenti

et al. 2024

Experimental Dermatology

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Hidradenitis suppurativa (HS) presents challenges in management due to its chronic nature and high risk of recurrence. Post‐surgical wound care plays a crucial role in treatment, even if standardized methods for assessing and predicting healing times are lacking. The aim of the study is to introduce the Wound Area Severity Index (WASI) as a novel tool to guide clinicians in assessing postsurgical wound progression and predicting potential healing times. A team of wound healing experts assessed 93 post‐surgical HS wounds resulting from wide excision and secondary intention healing. For each wound healing time, wound area, wound bed score (WBS), and WASI were evaluated. WASI includes four parameters: area, temperature, depth and wound Bed, each with four severity levels. The total WASI score ranges from 4 to 16. Spearman correlation and Kruskal–Wallis tests were employed for statistical analysis. WASI strongly correlated with wound healing time (rho: 0.813, p < 0.001). Higher WASI scores were associated with prolonged healing, while lower scores indicated almost healed wounds. The WASI score has proven to be more highly predictive of healing times when compared to the individual parameter of the Area (moderate positive correlation, r: 0.77) and the WBS (negative correlation, r: −0.72). A total WASI score of 4 corresponded to a median healing time of 7 days, while a score exceeding 9 suggested a median healing time of 56 days. WASI has proven to be a valuable tool for assessing and predicting healing times in post‐surgical HS wounds. Its simplicity, cost‐effectiveness, and ability to integrate multiple parameters make it a promising addition to wound care practice.

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Wound area severity index (WASI): A novel tool for assessing and predicting healing times in hidradenitis suppurativa postsurgical wounds

Michelucci,

Manzo Margiotta,

Innocenti

et al. 2024

Experimental Dermatology

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Empowering Patients and Caregivers to Use AI and Computer Vision for Wound Monitoring: A Feasibility Study (Preprint)

Raizmam,

Ramírez-GarciaLuna,

Newaz

et al. 2024

Preprint

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BACKGROUND Chronic wounds affect 1-2% of the global population, and pose significant health and quality-of-life challenges for patients and caregivers. Advances in artificial intelligence (AI) and computer vision (CV) technologies present new opportunities for enhancing wound care, particularly through remote monitoring and patient engagement. A Digital Wound Care Solution (DWCS) that objective wound tracking using AI/CV was redesigned as a patient-facing mobile application to empower patients and caregivers to actively participate in wound monitoring and management. OBJECTIVE This study aimed to evaluate the feasibility, usability, and preliminary clinical outcomes of the Patient Connect application in enabling patients and caregivers to remotely capture and share wound data with healthcare providers. METHODS A feasibility study was conducted at two outpatient clinics in Canada between May 2020 and February 2021. Twenty-eight patients with chronic wounds were recruited and trained to use the Patient Connect app for wound imaging and secure data sharing with their care teams. Wound images and data were analyzed using AI/CV models integrated into the app. Clinicians reviewed the data to inform treatment decisions during follow-up visits or remotely. Key metrics included app usage frequency, patient engagement, and wound closure rates. RESULTS Participants captured a median of 13 wound images per wound, with images submitted every 8 days on average. The study cohort included patients with diabetic ulcers, venous ulcers, pressure injuries, and post-surgical wounds. A median wound closure rate of 80% was achieved across all patients, demonstrating the app’s clinical potential. Feedback from patients and clinicians highlighted the app’s usability, data security features, and ability to enhance remote monitoring. CONCLUSIONS The Patient Connect application effectively engaged patients and caregivers in chronic wound care, demonstrating feasibility and promising clinical outcomes. By enabling secure, remote wound monitoring through AI/CV technology, the app has the potential to improve patient adherence, enhance care accessibility, and optimize clinical workflows. Future studies should focus on evaluating its scalability, cost-effectiveness, and broader applicability in diverse healthcare settings.

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A review of the artificial intelligence application as a guideline tool for the wound management

Lurin,

Gorobeiko,

Sokol

et al. 2024

ЕМ

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The global interest and substantial challenges on this subject contribute to its relevance. This analysis centers on the implementation of artificial intelligence within the medical field, with a specific focus on its application in managing wounds. Through an examination of numerous online studies and publications, we can gain insight into how artificial intelligence is being employed to enhance the diagnosis, treatment, and monitoring of wound healing. The integration of artificial intelligence in this sector has the capacity to transform medical practice by improving precision, effectiveness, and individualized patient care. As a result, it is a leading area of research and advancement on a global scale. We used the PubMed and Google Scholar electronic databases of medical publications, searching for abstracts using the following key phrases: artificial intelligence and wound management, artificial intelligence and gunshot wounds, artificial intelligence and war medicine, artificial intelligence and surgery. Based on search results, a literature analysis was performed. Conclusions. It is necessary to create numerous working groups of highly qualified specialists from each discipline and direction of medical activity, where the specific weight of each symptom, laboratory indicator, each radiological and ultrasound examination result is determined based on the data of real cases. And such work should have no less discipline and structure than medical research, it is optimal to get a universal software tool for this stage of work, which can be used with certain variations for the whole variety of pathological conditions and processes.

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Artificial Intelligence in Wound Care Education: Protocol for a Scoping Review

Cited by 4 publications

References 42 publications

Wound area severity index (WASI): A novel tool for assessing and predicting healing times in hidradenitis suppurativa postsurgical wounds

Wound area severity index (WASI): A novel tool for assessing and predicting healing times in hidradenitis suppurativa postsurgical wounds

Empowering Patients and Caregivers to Use AI and Computer Vision for Wound Monitoring: A Feasibility Study (Preprint)

A review of the artificial intelligence application as a guideline tool for the wound management

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