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Machine Learning Algorithms for Improved Glaucoma Diagnosis

Bizios, Dimitrios LU (2011) In Lund University, Faculty of Medicine Doctoral Dissertation Series 2011:70.
Abstract
Primary open angle glaucoma, one of the leading causes of blindness in the world, constitutes a slow progressing condition characterized by damage to the optic nerve and retinal nerve fibre layer, and results in visual field defects afflicting the visual function. Highly specific and sensitive diagnostic tests able to detect the clinically significant glaucomatous changes in the structure of the nerve fiber layer and visual field are therefore required for the early detection and management of this disease. This thesis treats the application of advanced statistical techniques based on machine learning for automated classification of tests from visual field examinations and retinal nerve fibre measurements to detect glaucoma. Diagnostic... (More)
Primary open angle glaucoma, one of the leading causes of blindness in the world, constitutes a slow progressing condition characterized by damage to the optic nerve and retinal nerve fibre layer, and results in visual field defects afflicting the visual function. Highly specific and sensitive diagnostic tests able to detect the clinically significant glaucomatous changes in the structure of the nerve fiber layer and visual field are therefore required for the early detection and management of this disease. This thesis treats the application of advanced statistical techniques based on machine learning for automated classification of tests from visual field examinations and retinal nerve fibre measurements to detect glaucoma. Diagnostic performance of the applied machine learning classification algorithms was shown to depend primarily on the type of test information that was provided. Optimized parameters from standard automated perimetry tests and OCT measurements of the nerve fibre layer derived from statistical processing to highlight statistically significant functional and structural changes, led to improvements in diagnostic accuracy. Moreover, the combination of structural and functional test information through incorporation of á priori knowledge about the anatomical relationship of the retinal nerve fibre layer and the visual field further increased the diagnostic performance of the automated classification algorithms. Machine Learning Classifiers based on optimized test input data could become useful decision support tools for more accurate glaucoma diagnosis. (Less)
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author
supervisor
opponent
  • Professor Tuulonen, Anja, Department of Ophthalmology, University of Oulu, Finland
organization
publishing date
type
Thesis
publication status
published
subject
in
Lund University, Faculty of Medicine Doctoral Dissertation Series
volume
2011:70
pages
60 pages
publisher
Department of Clinical Sciences, Lund University
defense location
MFC Lilla Aulan, Ingång 59, Skånes Universitetssjukhus, Malmö
defense date
2011-10-15 09:15
ISSN
1652-8220
ISBN
978-91-86871-20-8
language
English
LU publication?
yes
id
91fbd7c6-582e-4073-839f-9ac1e0cda576 (old id 2167214)
date added to LUP
2011-09-27 09:38:30
date last changed
2016-09-19 08:44:49
@phdthesis{91fbd7c6-582e-4073-839f-9ac1e0cda576,
  abstract     = {Primary open angle glaucoma, one of the leading causes of blindness in the world, constitutes a slow progressing condition characterized by damage to the optic nerve and retinal nerve fibre layer, and results in visual field defects afflicting the visual function. Highly specific and sensitive diagnostic tests able to detect the clinically significant glaucomatous changes in the structure of the nerve fiber layer and visual field are therefore required for the early detection and management of this disease. This thesis treats the application of advanced statistical techniques based on machine learning for automated classification of tests from visual field examinations and retinal nerve fibre measurements to detect glaucoma. Diagnostic performance of the applied machine learning classification algorithms was shown to depend primarily on the type of test information that was provided. Optimized parameters from standard automated perimetry tests and OCT measurements of the nerve fibre layer derived from statistical processing to highlight statistically significant functional and structural changes, led to improvements in diagnostic accuracy. Moreover, the combination of structural and functional test information through incorporation of á priori knowledge about the anatomical relationship of the retinal nerve fibre layer and the visual field further increased the diagnostic performance of the automated classification algorithms. Machine Learning Classifiers based on optimized test input data could become useful decision support tools for more accurate glaucoma diagnosis.},
  author       = {Bizios, Dimitrios},
  isbn         = {978-91-86871-20-8},
  issn         = {1652-8220},
  language     = {eng},
  pages        = {60},
  publisher    = {Department of Clinical Sciences, Lund University},
  school       = {Lund University},
  series       = {Lund University, Faculty of Medicine Doctoral Dissertation Series},
  title        = {Machine Learning Algorithms for Improved Glaucoma Diagnosis},
  volume       = {2011:70},
  year         = {2011},
}