Healthcare-associated infections resulting from cross-contamination, particularly from the hands of multidisciplinary staff, significantly impact patient mortality in health units. The prolonged nature of classical phenotypic diagnostic methods underscores the need for faster and more precise alternatives. This study proposes an automatic procedure utilizing hyperspectral imaging (HSI) in shortwave infrared (SWIR) range to detect resistance to oxycillins in hospital bacteria. The automatic procedure employs partial least square with discriminant analysis (PLS-DA) for the classification of antibiotic-resistant and non-resistant bacteria. HSI data were obtained from samples collected from hands of eight intensive care unit workers using sisuCHEMA workstation. Results demonstrated effectiveness of proposed procedure in detecting oxycillin resistance in S. aureus samples and other 14 strains of Staphylococcus spp.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC53108.2024.10781695 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning.
Food Chem X
February 2025
Food Science College, Xizang Agriculture & Animal Husbandry University, R&D Center of Agricultural Products with Xizang Plateau Characteristics, The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China.
Commercial jerky counterfeiting is widespread in the market. This study combined visible-near-infrared and short-wave-near-infrared hyperspectral imaging along with multiple machine learning algorithms for non-destructive identification of five types of commercial jerky products, and explored the impact of different spectral bands, algorithm selection, and optimization methods on identification performance. After data preprocessing, all models' accuracies and stability improved.
View Article and Find Full Text PDFImproving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.
Sci Rep
March 2025
Brussel Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, 1050, Brussels, Belgium.
Lung cancer remains a major global health challenge, and accurate pathological examination is crucial for early detection. This study aims to enhance hyperspectral pathological image analysis by refining annotations at the cell level and creating a high-quality hyperspectral dataset of lung tumors. We address the challenge of coarse manual annotations in hyperspectral lung cancer datasets, which limit the effectiveness of deep learning models requiring precise labels for training.
View Article and Find Full Text PDFAdvances of Vis/NIRS and imaging techniques assisted by AI for tea processing.
Crit Rev Food Sci Nutr
March 2025
National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, P.R. China.
Tea is one of the most popular drinks due to its distinct flavor and numerous health benefits. The quality of tea is closely related to production processing. Human sensory evaluation is the conventional method for quality monitoring in tea processing.
View Article and Find Full Text PDFHyperspectral discrimination of vegetable crops grown under organic and conventional cultivation practices: a machine learning approach.
Sci Rep
March 2025
Department of Organic Agriculture, Kerala Agricultural University, Thiruvananthapuram, Kerala, 695522, India.
A verifiable and regional level method for mapping crops cultivated under organic practices holds significant promise for certifying and ensuring the quality of farm products marketed as organic. The prevailing method for the identification of organic crops involves labor-intensive manual inspections, detailed record-keeping of crop stages, and certification. Hyperspectral remote sensing is an evolving general sensing technique for extracting crop information across various scales.
View Article and Find Full Text PDFInk classification in historical documents using hyperspectral imaging and machine learning methods.
Spectrochim Acta A Mol Biomol Spectrosc
February 2025
Department of Optics, University of Granada, Faculty of Sciences, Campus Fuentenueva, s/n, Granada, 18071, Spain.
Ink identification using only spectral reflectance information poses significant challenges due to material degradation, aging, and spectral overlap between ink classes. This study explores the use of hyperspectral imaging and machine learning techniques to classify three distinct types of inks: pure metallo-gallate, carbon-containing, and non-carbon-containing inks. Six supervised classification models, including five traditional algorithms (Support Vector Machines, K-Nearest Neighbors, Linear Discriminant Analysis, Random Forest, and Partial Least Squares Discriminant Analysis) and one Deep Learning-based model, were evaluated.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!