Introduction: Clinical signs and symptoms (CSS) of infection are a standard part of wound care, yet they can have low specificity and sensitivity, which can further vary due to clinician knowledge, experience, and education. Wound photography is becoming more widely adopted to support wound care. Thermography has been studied in the medical literature to assess signs of perfusion and inflammation for decades. Bacterial fluorescence has recently emerged as a valuable tool to detect a high bacterial load within wounds. Combining these modalities offers a potential objective screening tool for wound infection.
Methods: A multi-center prospective study of 66 outpatient wound care patients used hyperspectral imaging to collect visible light, thermography, and bacterial fluorescence images. Wounds were assessed and screened using the International Wound Infection Institute (IWII) checklist for CSS of infection. Principal component analysis was performed on the images to identify wounds presenting as infected, inflamed, or non-infected.
Results: The model could accurately predict all three wound classes (infected, inflamed, and non-infected) with an accuracy of 74%. They performed best on infected wounds (100% sensitivity and 91% specificity) compared to non-inflamed (sensitivity 94%, specificity 70%) and inflamed wounds (85% sensitivity, 77% specificity).
Discussion: Combining multiple imaging modalities enables the application of models to improve wound assessment. Infection detection by CSS is vulnerable to subjective interpretation and variability based on clinicians' education and skills. Enabling clinicians to use point-of-care hyperspectral imaging may allow earlier infection detection and intervention, possibly preventing delays in wound healing and minimizing adverse events.
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http://dx.doi.org/10.3389/fmed.2023.1165281 | DOI Listing |
Curr Res Food Sci
December 2024
Empa Swiss Federal Laboratories for Material Science and Technology, ETH Zurich, Lerchenfeldstrasse 5, 9014, St. Gallen, Switzerland.
This study detected the macronutrients retained in glutinous rice (GR) under different drying conditions by innovatively applying visible-near infrared hyperspectral imaging coupled with different spectra preprocessing and effective wavelength selection techniques (EWs). Subsequently, predictive models were developed based on processed spectra for the detection of the macronutrients, which include protein content (PC), moisture content (MC), fat content (FC), and ash content (AC). The result shows the raw spectra-based model had a prediction accuracy ( ) of 0.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
December 2024
Jiangsu Dualix Spectral Imaging Co., Ltd. Wuxi 214000, China.
This study aims to establish a rapid and non-destructive method for recognizing the origins and cultivation patterns of Astragali Radix. A hyperspectral imaging system(spectral ranges: 400-1 000 nm, 900-1 700 nm; detection time: 15 s) was used to examine the samples of Astragali Radix with different origins and cultivation patterns. The collected hyperspectral datasets were highly correlated and numerous, which required the establishment of stable and reliable dimension reduction and classification models.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
December 2024
Key Laboratory of Modern Preparation of TCM,Ministry of Education, Jiangxi University of Chinese Medicine Nanchang 330004, China National Key Laboratory of Creation of Modern Chinese Medicine with Classical Formulas Nanchang 330004, China.
In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations.
View Article and Find Full Text PDFChem Biodivers
January 2025
Department of Horticultural Science, Faculty of Agriculture, Jahrom University, Jahrom, Iran.
The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root.
View Article and Find Full Text PDFCurr Res Food Sci
December 2024
Department of Food and Nutrition, University of Helsinki, P.O. Box 66, 00014, Helsinki, Finland.
Modified Atmosphere Packaging (MAP) is a conventional method used to prolong the shelf-life of fresh-cut vegetables, including lettuce. However, MAP-stored lettuce remains perishable, and its deterioration mechanism is not fully understood. Here, we utilized non-targeted LC-MS metabolomics to evaluate the effects of cutting and extended storage time on metabolite profiles of lettuce stored in MAP.
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