Clinical validation of a smartphone application for automated wound measurement in patients with venous leg ulcers

Fong, Khi Yung; Lai, Tina Peiting; Chan, Kwai S.; See, Isabel Jia Le; Goh, Cheng Cheng; Muthuveerappa, Sivakami; Tan, Audrey Huimin; Liang, Shanying; Lo, Zhiwen Joseph

doi:10.1111/iwj.13918

Cited by 9 publications

(7 citation statements)

References 34 publications

(78 reference statements)

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“…A number of software companies have developed smartphone apps to measure and record wound size, including for DFU ( 15 , 21 – 25 ). Remote wound monitoring programs may be helpful in the management of chronic wounds, particularly as a communication tool between patients and their healthcare providers when close follow-up is necessary ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

Implementation of a patient-centered remote wound monitoring system for management of diabetic foot ulcers

Keegan,

Bose,

McDermott

et al. 2023

Front. Endocrinol.

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BackgroundRegular clinical assessment is critical to optimize lower extremity wound healing. However, family and work obligations, socioeconomic, transportation, and time barriers often limit patient follow-up. We assessed the feasibility of a novel, patient-centered, remote wound management system (Healthy.io Minuteful for Wound Digital Management System) for the surveillance of lower extremity wounds.MethodsWe enrolled 25 patients from our outpatient multidisciplinary limb preservation clinic with a diabetic foot ulcer, who had undergone revascularization and podiatric interventions prior to enrollment. Patients and their caregivers were instructed on how to use the digital management system and asked to perform one at-home wound scan per week for a total of 8 weeks using a smartphone application. We collected prospective data on patient engagement, smartphone app useability, and patient satisfaction.ResultsTwenty-five patients (mean age 65.5 ± 13.7 years, 60.0% male, 52.0% Black) were enrolled over 3 months. Mean baseline wound area was 18.0 ± 15.2 cm2, 24.0% of patients were recovering from osteomyelitis, and post-surgical WiFi stage was 1 in 24.0%, 2 in 40.0%, 3 in 28.0%, and 4 in 8.00% of patients. We provided a smartphone to 28.0% of patients who did not have access to one that was compatible with the technology. Wound scans were obtained by patients (40.0%) and caregivers (60.0%). Overall, 179 wound scans were submitted through the app. The mean number of wound scans acquired per patient was 0.72 ± 0.63 per week, for a total mean of 5.80 ± 5.30 scans over the course of 8 weeks. Use of the digital wound management system triggered an early change in wound management for 36.0% of patients. Patient satisfaction was high; 94.0% of patients reported the system was useful.ConclusionThe Healthy.io Minuteful for Wound Digital Management System is a feasible means of remote wound monitoring for use by patients and/or their caregivers.

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Section: Discussionmentioning

confidence: 99%

Implementation of a patient-centered remote wound monitoring system for management of diabetic foot ulcers

Keegan,

Bose,

McDermott

et al. 2023

Front. Endocrinol.

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“…A total of 2610 literatures were retrieved from the preliminary search. After step‐by‐step screening, 14 literatures 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 met the inclusion criteria. The literature screening process is shown in Figure 1 .…”

Section: Resultsmentioning

confidence: 99%

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

Wu,

2024

International Wound Journal

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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.

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“…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%

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

Lo,

Mak,

Liang

et al. 2023

International Wound Journal

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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.

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Clinical validation of a smartphone application for automated wound measurement in patients with venous leg ulcers

Cited by 9 publications

References 34 publications

Implementation of a patient-centered remote wound monitoring system for management of diabetic foot ulcers

Implementation of a patient-centered remote wound monitoring system for management of diabetic foot ulcers

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

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

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