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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. [ABSTRACT FROM AUTHOR]

Diabetic Foot in Primary and Tertiary (DEFINITE) Care is an inter‐institutional and multi‐disciplinary team (MDT) health systems innovation programme at a healthcare cluster in Singapore. We aim to achieve coordinated MDT care across primary and tertiary care for patients with diabetic foot ulcers (DFU), within our public healthcare cluster ‐ an integrated network of seven primary care polyclinics and two acute care tertiary hospitals (1700‐bed and 800‐bed) with a total catchment population of 2.2 million residents. Results from prospective DEFINITE Care is referenced against a retrospective 2013–2017 cohort, which was previously published. Cardiovascular profile of the study population is compared against the same population's profile in the preceding 12 months. Between June 2020 and December 2021, there were 3475 unique patients with DFU with mean age at 65.9 years, 61.2% male, mean baseline HbA1c at 8.3% with mean diabetes duration at 13.3 years, mean diabetes complication severity index (DCSI) at 5.6 and mean Charlson Comorbidity Index (CCI) at 6.8. In the 12‐months preceding enrolment to DEFINITE Care, 35.5% had surgical foot debridement, 21.2% had minor lower extremity amputation (LEA), 7.5% had major LEA whilst 16.8% had revascularisation procedures. At 18‐months after the implementation of DEFINITE Care programme, the absolute minor and major amputation rates were 8.7% (n = 302) and 5.1% (n = 176), respectively, equating to a minor and major LEA per 100000 population at 13.7 and 8.0, respectively. This represents an 80% reduction in minor amputation rates (P <.001) and a 35% reduction in major amputation rates (P =.005) when referenced against a retrospective 2013–2017 cohort, which had minor and major LEA per 100000 population at 68.9 and 12.4, respectively. As compared to the preceding 12 months, there was also a significant improvement in cardiovascular profile (glycemic and lipid control) within the DEFINITE population, with improved mean HbAc1 (7.9% from 8.4%, P <.001), low‐density lipoprotein (LDL) levels (2.1 mmol/L from 2.2, P <.001), total cholesterol (3.9 mmol/L from 4.1, P <.001) and triglycerides levels (1.6 mmol/L from 1.8, P =.002). Multivariate analysis revealed a history of minor amputation in the preceding 12 months to be an independent predictor for major and minor amputation within the study period of 18 months (Hazard Ratio 3.4 and 1.8, respectively, P <.001). In conclusion, within DEFINITE care, 18‐month data showed a significant reduction of minor and major LEA rates, with improved medical optimisation and cardiovascular profile within the study population. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Present guidelines recommend a multidisciplinary team (MDT) approach to diabetic foot ulcer (DFU) care, but relevant data from Asia are lacking. We aim to evaluate the clinical and economic outcomes of an MDT approach in a lower extremity amputation prevention programme (LEAPP) for DFU care in an Asian population. We performed a case‐control study of 84 patients with DFU between January 2017 and October 2017 (retrospective control) vs 117 patients with DFU between December 2017 and July 2018 (prospective LEAPP cohort). Comparing the clinical outcomes between the retrospective cohort and the LEAPP cohort, there was a significant decrease in mean time from referral to index clinic visit (38.6 vs 9.5 days, P <.001), increase in outpatient podiatry follow‐up (33% vs 76%, P <.001), decrease in 1‐year minor amputation rate (14% vs 3%, P =.007), and decrease in 1‐year major amputation rate (9% vs 3%, P =.05). Simulation of cost avoidance demonstrated an annualised cost avoidance of USD $1.86m (SGD $2.5m) for patients within the LEAPP cohort. In conclusion, similar to the data from Western societies, an MDT approach in an Asian population, via a LEAPP for patients with DFU, demonstrated a significant reduction in minor and major amputation rates, with annualised cost avoidance of USD $1.86m. [ABSTRACT FROM AUTHOR]

Introduction: Mycotic aortic aneurysm (MAA) is a life-threatening condition. Endovascular repair (EVAR) of aortic aneurysms has been found to be a safe and effective alternative to open repair. We aimed to present the short- to medium-term outcomes for EVAR of MAA in our cohort.Methods: We conducted a retrospective study of 23 consecutive patients with MAA who underwent EVAR in our hospital from January 2008 to July 2017.Results: The mean age of our study population was 62 years. The mean aneurysmal size was 3.2 cm. Abdominal MAA (n = 16, 70%) were the most common, followed by thoracic MAA (n = 4, 17%). There was no 30-day mortality in our cohort. Endoleak (Types 1, 3, 4) was detected in 3 (13%) cases. At the one-month surveillance computed tomography aortogram, all patients had a reduction in aneurysmal size and 5 (22%) had complete aneurysmal sac resolution. 7 (30%) patients had sac resolution at six months and 8 (35%) patients at 12 months. Overall survival was 91%, 80% and 61% at one, 12 and 60 months, respectively.Conclusion: EVAR is a feasible and durable method for the repair of MAA, with a five-year overall survival of 61%. All patients in our study had a reduction in aneurysmal size at one month, with 65% having complete aneurysmal sac resolution by 12 months. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]