Clinical validation of a <scp>machine‐learning</scp>‐based handheld <scp>3‐dimensional</scp> infrared wound imaging device in venous leg ulcers

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.

Section: Resultsmentioning

confidence: 99%

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

Wu,

2024

“…Commercial systems are available with good inter‐rated reliability, but often require the use of proprietary equipment or software. Examples include Tissue Analytics (Net Health Systems Inc, Florida, USA) for venous leg ulcer measurement, which requires the use of green dot and video capture to analyse the wound, 48 and WoundAide (Konica Minolta Inc, Tokyo, Japan) which requires use of dedicated infrared device 49 . The CARES4WOUNDS (Tetsuyu, Singapore) system for monitoring diabetic foot ulcers requires a dedicated sensor or a specific smartphone model 50 .…”

Section: Discussionmentioning

confidence: 99%

“…Examples include Tissue Analytics (Net Health Systems Inc, Florida, USA) for venous leg ulcer measurement, which requires the use of green dot and video capture to analyse the wound, 48 and WoundAide (Konica Minolta Inc, Tokyo, Japan) which requires use of dedicated infrared device. 49 The CARES4WOUNDS (Tetsuyu, Singapore) system for monitoring diabetic foot ulcers requires a dedicated sensor or a specific smartphone model. 50 The images from our dataset consisted of wound images taken by medical personnel for photo documentation during clinical practice, with significant variation in the device used for image capture, lighting conditions, photo angles, image size and quality.…”

Section: Discussionmentioning

confidence: 99%

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

Lo,

Mak,

Liang

et al. 2023

Self Cite

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.

“…Area was calculated by multiplying length and width. A detailed description of the method of wound measurement was described in our previous report 14 . Infected wounds were defined as the presence of purulent or malodorous discharge, peri‐wound tenderness and/or erythema.…”

Section: Methodsmentioning

confidence: 99%

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

Chan

Wang

et al. 2023

Self Cite

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.