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This study evaluated the effectiveness of a multi-disciplinary diabetic limb salvage programme in improving clinical outcomes and optimising healthcare utilisation in 406 patients aged ≥80 years with diabetic foot ulcers (DFUs), compared to 2392 younger patients enrolled from June 2020 to June 2021 and against 1716 historical controls using one-to-one propensity score matching. Results showed that elderly programme patients had lower odds of amputation-free survival (odds ratio: 0.64, 95% CI: 0.47, 0.88) and shorter cumulative length of stay (LOS) compared to younger programme patients (incidence rate ratio: 0.45, 95% CI: 0.29, 0.69). Compared to the matched controls, participating in the programme was associated with 5% higher probability of minor lower extremity amputation, reduced inpatient admissions and emergency visits, shorter LOS but increased specialist and primary care visits (all p-values <0.05). The findings suggest that the programme yielded favourable impacts on the clinical outcomes of patients aged≥80 years with DFUs. Further research is needed to develop specific interventions tailoring to the needs of the elderly population and to determine their effectiveness on patient outcomes while accounting for potential confounding factors.
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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