Machine Learning Algorithms for Improved Glaucoma Diagnosis

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.