ArticleBiomedical optics express2020
Single snapshot spatial frequency domain imaging for risk stratification of diabetes and diabetic foot.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology.Journal of biomedical optics · 2025Article
- Model for the diffuse reflectance in spatial frequency domain imaging.Journal of biomedical optics · 2023Article
- Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals.PloS one · 2023Article
- Article
- Effect of curvature correction on parameters extracted from hyperspectral images.Journal of biomedical optics · 2021Article
- Handheld spatial frequency domain imager for noninvasive Sjögren's syndrome labial salivary gland biopsy.Biomedical optics express · 2021Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic foot is one of the major complications of diabetes. In this work, a real-time Single Snapshot Multiple-frequency Demodulation (SSMD) - Spatial Frequency Domain Imaging (SFDI) system was used to image the forefoot of healthy volunteers, diabetes, and diabetic foot patients. A layered skin model was used to obtain the 2D maps of optical and physiological parameters, including cutaneous hemoglobin concentration, oxygen saturation, scattering properties, melanin content, and epidermal thickness, from every single snapshot. We observed a strong correlation between the measured optical and physiological parameters and the degree of diabetes. The cutaneous hemoglobin concentration, oxygen saturation, and epidermal thickness decrease, whereas the melanin content increases with the progress of diabetes. The melanin content further increases, and the reduced scattering coefficient and scattering power are lower for diabetic foot patients than those of both healthy and diabetic subjects. High accuracies (AUC) of 97.2% (distinguishing the diabetic foot patients among all subjects), 95.2% (separating healthy subjects from the diabetes patients), and 87.8% (classifying mild vs severe diabetes), respectively, are achieved in binary classifications in sequence using the SSMD-SFDI system, demonstrating its applicability to risk stratification of diabetes and diabetic foot. The prognostic value of the SSMD-SFDI system in the prediction of the occurrence of the diabetic foot and other applications in monitoring tissue microcirculation and peripheral vascular disease are also addressed.
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