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.

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

Chronic wounds are associated with significant clinical, economic and quality-of-life burden. Despite the variety of wound imaging systems available in the market for wound assessment and surveillance, few are clinically validated among patients of Asian ethnicity. We aimed to clinically validate the accuracy of a smartphone wound application (Tissue Analytics [TA], Net Health Systems Inc, Florida, USA), versus conventional wound measurements (visual approximation and paper rulers), in patients of Asian ethnicity with venous leg ulcers (VLU). A prospective cohort study of patients presenting with VLU to a specialist wound nurse clinic over a 5-week duration was conducted. Each patient received seven wound measurements: one by a trained wound nurse clinician, and three separate wound measurements using TA on each of the iOS and Android operating systems. Inter-rater and intra-rater reliability between clinical and TA-based measurements were analysed using intra-class correlation statistics, with values of <0.5, 0.5 to 0.75, 0.75 to 0.9, and >0.9 indicating poor, moderate, good and excellent reliability, respectively. 82 patients (51% males), with a mean age at 65.8 years, completed the 5-week study duration. 25 (30%) had underlying diabetes mellitus. Chinese, Malay and Indian ethnicity comprised 68%, 12% and 11%, respectively. The VLU healed in 26 (32%) of patients within the study period. In total, 358 wound episodes with 2334 wound images were analysed. Inter-rater reliability for length, width and area between wound nurse measurements and TA application measurements was good (range 0.799-0.919, P < 0.001). Separate measurements of intra-rater reliability for length, width and area within the iOS or Android systems were excellent (range 0.967-0.985 and range 0.977-0.984 respectively, P < 0.001). Inter-rater reliability between TA used on the iOS and Android systems was also excellent (0.987-0.989, P < 0.001). Tissue Analytics, a smartphone wound application, is a useful adjunct for wound assessment and surveillance in VLU patients of Asian ethnicity.

Clinical validation of an artificial intelligence-enabled wound imaging mobile application in diabetic foot ulcers.

There is a lifetime risk of 15% to 25% of development of diabetic foot ulcers (DFUs) in patients with diabetes mellitus. DFUs need to be followed up on and assessed for development of complications and/or resolution, which was traditionally performed using manual measurement. Our study aims to compare the intra- and inter-rater reliability of an artificial intelligence-enabled wound imaging mobile application (CARES4WOUNDS [C4W] system, Tetsuyu, Singapore) with traditional measurement. This is a prospective cross-sectional study on 28 patients with DFUs from June 2020 to January 2021. The main wound parameters assessed were length and width. For traditional manual measurement, area was calculated by overlaying traced wound on graphical paper. Intra- and inter-rater reliability was analysed using intra-class correlation statistics. A value of <0.5, 0.5-0.75, 0.75-0.9, and >0.9 indicates poor, moderate, good, and excellent reliability, respectively. Seventy-five wound episodes from 28 patients were collected and a total of 547 wound images were analysed in this study. The median wound area during the first clinic consultation and all wound episodes was 3.75 cm2 (interquartile range [IQR] 1.40-16.50) and 3.10 cm2 (IQR 0.60-14.84), respectively. There is excellent intra-rater reliability of C4W on three different image captures of the same wound (intra-rater reliability ranging 0.933-0.994). There is also excellent inter-rater reliability between three C4W devices for length (0.947), width (0.923), and area (0.965). Good inter-rater reliability for length, width, and area (range 0.825-0.934) was obtained between wound nurse measurement and each of the C4W devices. In conclusion, we obtained good inter-rater and intra-rater reliability of C4W measurements against traditional wound measurement. The C4W is a useful adjunct in monitoring DFU wound progress.

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

Chronic venous insufficiency is a chronic disease of the venous system with a prevalence of 25% to 40% in females and 10% to 20% in males. Venous leg ulcers (VLUs) result from venous insufficiency. VLUs have a prevalence of 0.18% to 1% with a 1-year recurrence of 25% to 50%, bearing significant socioeconomic burden. It is therefore important for regular assessment and monitoring of VLUs to prevent worsening. Our study aims to assess the intra- and inter-rater reliability of a machine learning-based handheld 3-dimensional infrared wound imaging device (WoundAide [WA] imaging system, Konica Minolta Inc, Tokyo, Japan) compared with traditional measurements by trained wound nurse. This is a prospective cross-sectional study on 52 patients with VLUs from September 2019 to January 2021 using three WA imaging systems. Baseline patient profile and clinical demographics were collected. Basic wound parameters (length, width and area) were collected for both traditional measurements and measurements taken by the WA imaging systems. Intra- and inter-rater reliability was analysed using intra-class correlation statistics. A total of 222 wound images from 52 patients were assessed. There is excellent intra-rater reliability of the WA imaging system on three different image captures of the same wound (intra-rater reliability ranging 0.978-0.992). In addition, there is excellent inter-rater reliability between the three WA imaging systems for length (0.987), width (0.990) and area (0.995). Good inter-rater reliability for length and width (range 0.875-0.900) and excellent inter-rater reliability (range 0.932-0.950) were obtained between wound nurse measurement and each of the WA imaging system. In conclusion, high intra- and inter-rater reliability was obtained for the WA imaging systems. We also obtained high inter-rater reliability of WA measurements against traditional wound measurement. The WA imaging system is a useful clinical adjunct in the monitoring of VLU wound documentation.

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.

A feasibility study on the efficacy of a patient-owned wound surveillance system for diabetic foot ulcer care (ePOWS study).

Objective: Wound image analysis tools hold promise in helping patients to monitor their wounds. We aim to perform a novel feasibility study on the efficacy of a patient-owned wound surveillance system for diabetic foot ulcer (DFU) care. Methods: This two-institutional, prospective, single-arm pilot study examined patients with DFU. An artificial intelligence-enabled image analysis app calculating the wound surface area was installed and patients or caregivers were instructed to take pictures of wounds during dressing changes. Patients were followed until wound deterioration, wound healing, or wound stability at 6 months occurred and the outcomes of interest included study adherence, algorithm performance, and user experience. Results: Between January 2021 and December 2021, 39 patients were enrolled in the study, with a mean age of 61.6 ± 8.6 years, and 69% (n = 27) of subjects were male. All patients had documented diabetes and 85% (n = 33) of them had peripheral arterial disease. A mean follow-up for those completing the study was 12.0 ± 8.5 weeks. At the conclusion of the study, 80% of patients (n = 20) had primary wound healing whilst 20% (n = 5) had wound deterioration. The study completion rate was 64% (n = 25). Usage of the app for surveillance of DFU healing, as compared to physician evaluation, yielded a sensitivity of 100%, specificity of 20%, positive predictive value of 83%, and negative predictive value of 100%. Of those who provided user experience feedback, 59% (n = 10) felt the app was easy to use, 47% (n = 8) would recommend the wound analysis app to others but only 6% would pay for the app out of pocket (n = 1). Conclusion: Implementation of a patient-owned wound surveillance system is feasible. Most patients were able to effectively monitor wounds using a smartphone app-based solution. The image analysis algorithm demonstrates strong performance in identifying wound healing and is capable of detecting deterioration prior to interval evaluation by a physician. Patients generally found the app easy to use but were reluctant to pay for the use of the solution out of pocket.

Prospective study on the clinical and economic burden of venous leg ulcers in the tropics.

OBJECTIVE: Venous leg ulcers (VLUs) are both chronic and recurrent. The treatment of such ulcers often require multiple outpatient visits and dressing changes. Several reports on the costs of treating such VLUs have been reported in the west. We prospectively evaluated the clinical and economic burden of VLUs in a population of Asian patients in the tropics. METHODS: Patients from a prospective two-center study conducted at two tertiary hospitals in Singapore, as a part of the Wound Care Innovation in the Tropics program, between August 2018 and September 2021 were recruited. The patients were followed up for 12 weeks (visit 1 to visit 12), until index ulcer healing, death, or lost to follow-up (whichever came first). These patients were then followed up 12 weeks later to determine the longer term outcome of the wound (healed, recurrence, remained unhealed). The itemized costs derived from the medical service were retrieved from the relevant departments of the study sites. The patients' health-related quality of life was assessed at baseline and the last visit of the 12-week follow-up period (or until index ulcer healing), using the official Singapore version of the EuroQol five-dimension-5L questionnaire, which also includes a visual analog scale (EQ-VAS). RESULTS: A total of 116 patients were enrolled; 63% were men, and the mean patient age was 64.7 years. Of the 116 patients, 85 (73%) had a healed ulcer at 24 weeks (mean duration to ulcer healing, 49 days), and 11 (12.9%) had experienced ulcer recurrence within the study period. Within the 6-month follow-up period, the mean direct healthcare cost per patient was USD$1998. The patients with healed ulcers had significantly lower costs per patient compared with those with unhealed ulcers (USD$1713 vs USD$2780). Regarding health-related quality of life, 71% of the patients had a lower quality of life at baseline, which had improved at 12 weeks of follow-up, with only 58% of the patients reported to have a lower quality of life. Also, the patients with healed ulcers scored higher for both utilities (societal preference weights) and EQ-VAS at follow-up (P < .001). In contrast, patients with unhealed ulcers only scored higher EQ-VAS at follow-up (P = .003). CONCLUSIONS: The findings from this exploratory study provide information on the clinical, quality of life, and economic burden of VLUs in an Asian population and suggest the importance of healing VLUs to reduce the effects on patients. The present study provides data as a basis for economic evaluation as a consideration for the treatment of VLUs.