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The purpose of this study was to compare the impact of hip arthroscopy group and open surgical dislocation group as treatments for femoroacetabular impingement (FAI) in young athletes, specifically in relation to early hip osteoarthritis. A systematic search was conducted across four databases to identify controlled trials comparing hip arthroscopy and open surgical dislocation for FAI treatment. The selected studies (9 in total) underwent rigorous literature assessment and data analysis using Review Manager (RevMan) 5.3 software. The meta- analysis revealed that there was no statistically significant difference between hip arthroscopy group (the test group) and the open surgical dislocation group (the control group) concerning the improvement of the alpha angle (Standardized Mean Difference [SMD]: -5.54; 95% Confidence Interval [CI]: - 12.45,1.38; p = 0.117), the Modified Harris Hip Score (mHHS) after a 12- month follow- up (SMD:0.94; 95% CI:- 2.87,4.75; p = 0.629) and the complication rate (OR: 0.66; 95% CI: 0.26,1.65; p = 0.372). However, the meta- analysis revealed that the Nonarthritic Hip Score (NAHS) after a 12- month follow- up of the test group was significantly higher than that of the control group (SMD: 6.31; 95% CI: 0.53, 12.09; p = 0.032). In terms of the reoperation rate, it demonstrated a significantly lower rate in the test group compared to the control group (OR: 0.48; 95% CI: 0.29, 0.82; p < 0.01). These findings suggest that hip arthroscopy may have better outcomes for patients with FAI, as it is associated with improvements in hip function and a lower reoperation rate. However, these conclusions should be validated by further high- quality studies.
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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