Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of diabetes science and technology
Artificial Intelligence for Diabetic Foot Screening Based on Digital Image Analysis: A Systematic Review
INTRODUCTION: Early detection of diabetic foot complications is essential for effective management and prevention of complications. Artificial intelligence (AI) technology based on digital image analysis offers a promising noninvasive method for diabetic foot screening. This systematic review aims to identify a study on the development of an AI model for diabetic foot screening using digital image analysis. METHODOLOGY: The review scrutinized articles published between 2018 and 2023, sourced from PubMed, ProQuest, and ScienceDirect. The keyword-based search resulted in 2214 relevant articles and nine articles that met the inclusion criteria. The article quality assessment was done through Quality Assessment of Diagnostic Accuracy Studies (QUADAS). Data were extracted and analyzed using NVivo. RESULTS: Thermal imagery or foot thermogram was the main data source, with plantar temperature distribution patterns as an important indicator. Deep learning methods, specifically artificial neural networks (ANNs) and convolutional neural networks (CNNs), are the most commonly used methods. The highest performance is demonstrated by the ANN model with MATLAB's Image Processing Toolbox that is able to classify each type of macula with 97.5% accuracy. The findings show the great potential of AI in improving the accuracy and efficiency of diabetic foot screening. CONCLUSION: This research provides important insights into the development of AI in digital image-based diabetic foot screening. Future studies need to focus on evaluating clinical applicability, including ethical aspects and patient data security, as well as developing more comprehensive data sets.
2 July 2026
Read appraisal →Journal of global health
Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals
BACKGROUND: Accurate assessment of pressure injury staging is essential for guiding appropriate care, reducing patient suffering, alleviating the healthcare burden, and improving quality of life. We aimed to develop a deep learning-based model for pressure injury recognition and to translate the best-performing model into a preliminary smartphone application. METHODS: We conducted a multicentre retrospective study using pressure injury images collected from 59 hospitals. We applied to the data set three artificial intelligence-based image analysis models, Mask R-convolutional neural network (CNN) with ResNet-18, Mask R-CNN with Swin Transformer, and Segmenting Objects by Locations, version 2. We assessed model performance using standard evaluation metrics, including precision-recall curves, average recall (AR), average precision (AP), and mean average precision (mAP). The model achieving the best overall performance was selected for subsequent translation into a smartphone-based tool. RESULTS: We included a total of 1903 pressure injury images, with 1713 used for model training and 190 for validation. Among the models evaluated, Mask R-CNN with Swin Transformer demonstrated the highest overall performance (mAP = 0.894, AP50 = 0.900, AP = 0.757, AR100 = 0.792), surpassing the corresponding metrics of the other two models. The model, based on Mask R-CNN with a Swin Transformer, was integrated into a smartphone for preliminary translation. CONCLUSIONS: The proposed deep learning-based system showed promising performance in pressure injury staging and may provide meaningful support for clinical decision-making. Future studies should focus on further validation and refinement using larger and more diverse data sets to enhance their applicability in routine clinical practice.
20 June 2026
Read appraisal →Journal of neural engineering
Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification
Objective.As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and improve clinical outcomes. Recently, Deep-learning methods based on structural MRI (sMRI) have been widely used for computer-aided AD diagnosis. However, reliable deployment remains challenging due to its sample-specific heterogeneity and the lack of explicit mechanisms for handling prediction uncertainty. In addition, common test-time perturbations and augmentation-based inference strategies could introduce anatomical inconsistency in medical images, leading to unstable decisions for hard cases.Approach.Accordingly, TriPathNet with Arbiter, as an uncertainty-aware multi-path framework, is proposed in the study. The framework learns spatially complementary representations via multi-view parallel encoding with metric-guided coordination. In addition, an augmentation-aware arbitration mechanism is introduced to adaptively correct predictions for high-uncertainty samples during inference.Main results.On the ADNI dataset for the AD vs. cognitively normal task, the proposed method achieves 98.85% accuracy, 99.18% sensitivity, 97.06% specificity, and 99.61% AUC, yielding satisfactory performance.Significance.The results indicate that TriPathNet with Arbiter provides an accurate and robust solution for sMRI-based computer-aided AD diagnosis, maintaining stable performance under test-time perturbations.
20 June 2026
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