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Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.

Publication ,  Journal Article
Belghith, A; Bowd, C; Medeiros, FA; Balasubramanian, M; Weinreb, RN; Zangwill, LM
Published in: Artif Intell Med
June 2015

UNLABELLED: Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases. METHOD: This paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class. RESULTS: The proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively). CONCLUSION: The use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

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Published In

Artif Intell Med

DOI

EISSN

1873-2860

Publication Date

June 2015

Volume

64

Issue

2

Start / End Page

105 / 115

Location

Netherlands

Related Subject Headings

  • Tomography, Optical Coherence
  • Time Factors
  • Reproducibility of Results
  • Predictive Value of Tests
  • Models, Statistical
  • Medical Informatics
  • Imaging, Three-Dimensional
  • Image Interpretation, Computer-Assisted
  • Humans
  • Glaucoma
 

Citation

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Belghith, A., Bowd, C., Medeiros, F. A., Balasubramanian, M., Weinreb, R. N., & Zangwill, L. M. (2015). Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression. Artif Intell Med, 64(2), 105–115. https://doi.org/10.1016/j.artmed.2015.04.002
Belghith, Akram, Christopher Bowd, Felipe A. Medeiros, Madhusudhanan Balasubramanian, Robert N. Weinreb, and Linda M. Zangwill. “Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.Artif Intell Med 64, no. 2 (June 2015): 105–15. https://doi.org/10.1016/j.artmed.2015.04.002.
Belghith A, Bowd C, Medeiros FA, Balasubramanian M, Weinreb RN, Zangwill LM. Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression. Artif Intell Med. 2015 Jun;64(2):105–15.
Belghith, Akram, et al. “Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression.Artif Intell Med, vol. 64, no. 2, June 2015, pp. 105–15. Pubmed, doi:10.1016/j.artmed.2015.04.002.
Belghith A, Bowd C, Medeiros FA, Balasubramanian M, Weinreb RN, Zangwill LM. Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression. Artif Intell Med. 2015 Jun;64(2):105–115.
Journal cover image

Published In

Artif Intell Med

DOI

EISSN

1873-2860

Publication Date

June 2015

Volume

64

Issue

2

Start / End Page

105 / 115

Location

Netherlands

Related Subject Headings

  • Tomography, Optical Coherence
  • Time Factors
  • Reproducibility of Results
  • Predictive Value of Tests
  • Models, Statistical
  • Medical Informatics
  • Imaging, Three-Dimensional
  • Image Interpretation, Computer-Assisted
  • Humans
  • Glaucoma