7 March 2016 Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods
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Abstract
Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.
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Hao Ding, Ming Cao, Andrew W. DuPont, Larry D. Scott, Sushovan Guha, Shashideep Singhal, Mamoun Younes, Isaac Pence, Alan Herline, David Schwartz, Hua Xu, Anita Mahadevan-Jansen, Xiaohong Bi, "Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods", Proc. SPIE 9704, Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry, 97040W (7 March 2016); doi: 10.1117/12.2225299; https://doi.org/10.1117/12.2225299
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