Clinical validation of a machine-learning-based handheld 3-dimensional infrared wound imaging device in venous leg ulcers

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

OBJECTIVE

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

Development of an explainable artificial intelligence model for Asian vascular wound images

Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.

The clinical value of intelligent wound measurement devices in patients with chronic wounds: A scoping review

Chronic wounds are common in clinical practice, with long treatment cycle and high treatment cost. Changes in wound area can well predict the effectiveness of treatment and the possibility of healing. Therefore, continuous wound monitoring and evaluation are particularly important. Traditional manual wound measurement tends to overestimate wound area. Recently, various intelligent wound measurement devices have been introduced into clinical practice. This review aims to summarise the reliability, validity, types and measurement principles of different intelligent wound measurement devices, so as to analyse the clinical value and application prospect. Articles numbering 2610 were retrieved from the database, and 14 articles met the inclusion criteria. The results showed that the intelligent wound measurement devices included in the study reported good reliability and validity. Contact devices can lead to wound bed damage, wound deformation, patient pain, and is not convenient for electronic wound recording; partial contact devices can complete continuous monitoring and recording of wounds, but are not sensitive to wound depth measurement. Non-contact devices are more accurate in capturing wound images. In addition to wound measurement, they also have the function of wound assessment. In general, handheld and portable non-contact devices have great clinical value and promotion prospects.

A prospective study on the wound healing and quality of life outcomes of patients with venous leg ulcers in Singapore-Interim analysis at 6 month follow up

Venous leg ulceration results in significant morbidity. However, the majority of studies conducted are on Western populations. This study aims to evaluate the wound healing and quality of life for patients with venous leg ulcers (VLUs) in a Southeast Asian population. This is a multi-centre prospective cohort study from Nov 2019 to Nov 2021. All patients were started on 2- or 4-layer compression bandage and were reviewed weekly or fortnightly. Our outcomes were wound healing, factors predictive of wound healing and the EuroQol 5-dimensional 5-level (EQ-5D-5L) health states. Within our cohort, there were 255 patients with VLU. Mean age was 65.2 ± 11.6 years. Incidence of diabetes mellitus was 42.0%. Median duration of ulcer at baseline was 0.30 years (interquartile range 0.136-0.834). Overall, the median time to wound healing was 4.5 months (95% confidence interval [CI]: 3.77-5.43). The incidence of complete wound healing at 3- and 6-month was 47.0% and 60.9%, respectively. The duration of the wound at baseline was independently associated with worse wound healing (Hazard ratio 0.94, 95% CI: 0.89-0.99, P = .014). Patients with healed VLU had a significantly higher incidence of perfect EQ-5D-5L health states at 6 months (57.8% vs 13.8%, P < .001). We intend to present longer term results in subsequent publications.

A prospective study on the efficacy of sequential treatment of technology Lipido-Colloid Impregnated with Silver and Technology Lipido-Colloid Nano-Oligosaccharide Factor in the management of venous leg ulcers

BACKGROUND AND AIMS

Venous leg ulcers (VLUs) are associated with significant morbidity and poor quality of life (QOL). Compression therapy and wound dressing are the mainstay treatment options. Technology Lipido-Colloid Impregnated with Silver (TLC-Ag) reduces bacterial load and Technology Lipido-Colloid Nano-Oligosaccharide Factor (TLC-NOSF) reduces elevated matrix metalloproteinases and improve wound healing. However, evidence is scarce on the role of sequential therapy. This study aims to evaluate if sequential treatment with TLC-Ag and TLC-NOSF improves VLU wound healing and QOL.

METHODS

This is a prospective cohort study from May 2020 to October 2021 on patients with VLUs who received sequential therapy, consisting of 2 weeks of TLC-Ag followed by two-layer compression bandage (2LB) with TLC-NOSF until complete wound healing. Participants were followed-up with weekly dressing changes. Our primary outcomes were wound area reduction (WAR) and Pressure Ulcer Scale of Healing (PUSH) score. Our secondary outcomes were QOL measures.

RESULTS

There were 28 patients with 57.1% males (n = 16) with a mean age of 65.3 years. Mean duration of VLU was 13.9 ± 11.7 weeks before the initiation of sequential therapy. Mean baseline wound area was 8.44 cm2. Median time to wound healing was 10 weeks. 57.1% of patients achieved complete wound closure at 3 months. There was significant WAR after 1 month (mean area 8.44-5.81 cm2, 31.2% decrease) and after 3 months (mean area 8.44-2.53 cm2, 70.0% decrease). Mean monthly WAR was 28.9%. PUSH score also decreased at 1 month (16.5% decrease, p < 0.001) and 3 months (63.3% decrease, p < 0.001) marks following the sequential therapy. EuroQol Visual Analog Scale (EQ-VAS) improved following sequential therapy (baseline: 69.0 ± 15.0, week 13: 80.2 ± 13.2, p < 0.001).

CONCLUSION

Sequential therapy with TLC-Ag followed by TLC-NOSF and 2LB is feasible, with good wound healing and improvement in QOL of patients with VLUs.

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.