Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

Hao Ding; Ming Cao; Andrew W. Dupont; Larry D. Scott; Sushovan Guha; Shashideep Singhal; Mamoun Younes; Isaac Pence; Alan Joseph Herline; David Schwartz; Hua Xu; Anita Mahadevan-Jansen; Xiaohong Bi

doi:10.1117/12.2225299

Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

Hao Ding, Ming Cao, Andrew W. Dupont, Larry D. Scott, Sushovan Guha, Shashideep Singhal, Mamoun Younes, Isaac Pence, Alan Joseph Herline, David Schwartz, Hua Xu, Anita Mahadevan-Jansen, Xiaohong Bi

Surgery

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	Biomedical Vibrational Spectroscopy 2016
Subtitle of host publication	Advances in Research and Industry
Editors	Anita Mahadevan-Jansen, Wolfgang Petrich
Publisher	SPIE
ISBN (Electronic)	9781628419382
DOIs	https://doi.org/10.1117/12.2225299
State	Published - 2016
Event	Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry - San Francisco, United States Duration: Feb 13 2016 → Feb 14 2016

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	9704
ISSN (Print)	1605-7422

Other

Other	Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry
Country/Territory	United States
City	San Francisco
Period	2/13/16 → 2/14/16

Keywords

Crohn's Colitis
Inflammatory Bowel Disease
Raman spectroscopy
Ulcerative Colitis
feature selection
multivariate analysis

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Biomaterials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.2225299

Cite this

Ding, H., Cao, M., Dupont, A. W., Scott, L. D., Guha, S., Singhal, S., Younes, M., Pence, I., Herline, A. J., Schwartz, D., Xu, H., Mahadevan-Jansen, A., & Bi, X. (2016). Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods. In A. Mahadevan-Jansen, & W. Petrich (Eds.), Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry Article 97040W (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9704). SPIE. https://doi.org/10.1117/12.2225299

Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods. / Ding, Hao; Cao, Ming; Dupont, Andrew W. et al.
Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry. ed. / Anita Mahadevan-Jansen; Wolfgang Petrich. SPIE, 2016. 97040W (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9704).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ding, H, Cao, M, Dupont, AW, Scott, LD, Guha, S, Singhal, S, Younes, M, Pence, I, Herline, AJ, Schwartz, D, Xu, H, Mahadevan-Jansen, A & Bi, X 2016, Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods. in A Mahadevan-Jansen & W Petrich (eds), Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry., 97040W, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 9704, SPIE, Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry, San Francisco, United States, 2/13/16. https://doi.org/10.1117/12.2225299

Ding H, Cao M, Dupont AW, Scott LD, Guha S, Singhal S et al. Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods. In Mahadevan-Jansen A, Petrich W, editors, Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry. SPIE. 2016. 97040W. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2225299

Ding, Hao ; Cao, Ming ; Dupont, Andrew W. et al. / Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods. Biomedical Vibrational Spectroscopy 2016: Advances in Research and Industry. editor / Anita Mahadevan-Jansen ; Wolfgang Petrich. SPIE, 2016. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).

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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.",

keywords = "Crohn's Colitis, Inflammatory Bowel Disease, Raman spectroscopy, Ulcerative Colitis, feature selection, multivariate analysis",

author = "Hao Ding and Ming Cao and Dupont, {Andrew W.} and Scott, {Larry D.} and Sushovan Guha and Shashideep Singhal and Mamoun Younes and Isaac Pence and Herline, {Alan Joseph} and David Schwartz and Hua Xu and Anita Mahadevan-Jansen and Xiaohong Bi",

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AU - Singhal, Shashideep

AU - Younes, Mamoun

AU - Pence, Isaac

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AU - Schwartz, David

AU - Xu, Hua

AU - Mahadevan-Jansen, Anita

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N2 - 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.

AB - 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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Discrimination of inflammatory bowel disease using Raman spectroscopy and linear discriminant analysis methods

Abstract

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Keywords

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