Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Advanced materials (Deerfield Beach, Fla.)
Advances in Wearable Bioimaging
The development of wearable bioimaging platforms has accelerated in the era of precision medicine, addressing the limitations of conventional, facility-based systems, which can be costly and difficult to access. In this review, we first outline the operating principles of major imaging modalities, with an emphasis on target tissues and factors that contribute to image quality. Next, we survey advances in miniaturizing these modalities into wearable devices, from electrical impedance tomography belts to flexible ultrasound and photoacoustic patches, highlighting materials selection and fabrication strategies that enhance image quality, skin adhesion, and functional integration. Lastly, we discuss outstanding technical and translational challenges that must be addressed to realize the full potential of these platforms, including safety and long-term use. With sustained research and responsible commercialization, these wearable devices could reshape continuous physiological monitoring and enable more proactive, preventive health management.
3 July 2026
Read appraisal →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
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