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Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography.
Invest Ophthalmol Vis Sci. 2005 Nov; 46(11):4121-9.IO

Abstract

PURPOSE

To develop and compare the ability of several automated classifiers to differentiate between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from Stratus optical coherence tomography (OCT; Carl Zeiss Meditec Inc., Dublin, CA) in a Chinese population in Taiwan.

METHODS

One randomly selected eye from each of 89 patients with glaucoma and each of 100 age- and sex-matched normal individuals were included in the study. Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head analysis results) were obtained by Stratus OCT. With the Stratus OCT parameters used as input, receiver operative characteristic (ROC) curves were generated by three methods, to classify eyes as either glaucomatous or normal: linear discriminant analysis (LDA), Mahalanobis distance (MD), and artificial neural network (ANN). The area under the ROC curve was optimized by principal component analysis (PCA). Classification accuracy was determined by cross validation.

RESULTS

The average visual field mean deviation was -0.7 +/- 0.6 dB in the normal group and -2.7 +/- 1.9 dB in the glaucoma group. The areas under the ROC curves were 0.824 (LDA), 0.849 (MD), 0.821 (ANN), 0.915 (LDA with PCA), 0.991 (MD with PCA), and 0.874 (ANN with PCA).

CONCLUSIONS

With Stratus OCT parameters used as input, automated classifiers show promise for discriminating between glaucomatous and normal eyes. MD measured from multivariate data can predict the severity of glaucoma through the construction of a measurement space. After PCA, implementation results show that the Mahalanobis space created by MD surpasses LDA and ANN in diagnosing glaucoma.

Authors+Show Affiliations

Department of Industrial Engineering and Management, National Chin-Yi Institute of Technology, Taichung County, Taiwan.No affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

16249489

Citation

Huang, Mei-Ling, and Hsin-Yi Chen. "Development and Comparison of Automated Classifiers for Glaucoma Diagnosis Using Stratus Optical Coherence Tomography." Investigative Ophthalmology & Visual Science, vol. 46, no. 11, 2005, pp. 4121-9.
Huang ML, Chen HY. Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. Invest Ophthalmol Vis Sci. 2005;46(11):4121-9.
Huang, M. L., & Chen, H. Y. (2005). Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. Investigative Ophthalmology & Visual Science, 46(11), 4121-9.
Huang ML, Chen HY. Development and Comparison of Automated Classifiers for Glaucoma Diagnosis Using Stratus Optical Coherence Tomography. Invest Ophthalmol Vis Sci. 2005;46(11):4121-9. PubMed PMID: 16249489.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. AU - Huang,Mei-Ling, AU - Chen,Hsin-Yi, PY - 2005/10/27/pubmed PY - 2005/12/22/medline PY - 2005/10/27/entrez SP - 4121 EP - 9 JF - Investigative ophthalmology & visual science JO - Invest Ophthalmol Vis Sci VL - 46 IS - 11 N2 - PURPOSE: To develop and compare the ability of several automated classifiers to differentiate between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from Stratus optical coherence tomography (OCT; Carl Zeiss Meditec Inc., Dublin, CA) in a Chinese population in Taiwan. METHODS: One randomly selected eye from each of 89 patients with glaucoma and each of 100 age- and sex-matched normal individuals were included in the study. Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head analysis results) were obtained by Stratus OCT. With the Stratus OCT parameters used as input, receiver operative characteristic (ROC) curves were generated by three methods, to classify eyes as either glaucomatous or normal: linear discriminant analysis (LDA), Mahalanobis distance (MD), and artificial neural network (ANN). The area under the ROC curve was optimized by principal component analysis (PCA). Classification accuracy was determined by cross validation. RESULTS: The average visual field mean deviation was -0.7 +/- 0.6 dB in the normal group and -2.7 +/- 1.9 dB in the glaucoma group. The areas under the ROC curves were 0.824 (LDA), 0.849 (MD), 0.821 (ANN), 0.915 (LDA with PCA), 0.991 (MD with PCA), and 0.874 (ANN with PCA). CONCLUSIONS: With Stratus OCT parameters used as input, automated classifiers show promise for discriminating between glaucomatous and normal eyes. MD measured from multivariate data can predict the severity of glaucoma through the construction of a measurement space. After PCA, implementation results show that the Mahalanobis space created by MD surpasses LDA and ANN in diagnosing glaucoma. SN - 0146-0404 UR - https://www.unboundmedicine.com/medline/citation/16249489/Development_and_comparison_of_automated_classifiers_for_glaucoma_diagnosis_using_Stratus_optical_coherence_tomography_ L2 - https://iovs.arvojournals.org/article.aspx?doi=10.1167/iovs.05-0069 DB - PRIME DP - Unbound Medicine ER -