Identification of Glaucoma Stages with Artificial Neural Networks Using Retinal Nerve Fibre Layer Analysis and Visual Field Parameters

For the diagnosis of glaucoma, we propose a system of Artificial Intelligence that employs Artificial Neural Networks (ANN) and integrates the analysis of the nerve fibres of the retina from the study with scanning laser polarimetry (NFAII;GDx), perimetry and clinical data. The present work shows an analysis of 106 eyes of 53 patients, in accordance with the stage of glaucomatous illness in which each eye was found. The groups defined include stage 0, which corresponds to normal eyes; stage 1, for ocular hypertension; 2, for early glaucoma; 3, for established glaucoma; 4, for advanced glaucoma and 5, for terminal glaucoma. The developed ANN is a multilayer perceptron provided with the Levenberg-Marquardt method. The learning was carried out with half of the data and with the training function of gradient descent w/momentum backpropagation and was checked by the diagnosis of a glaucoma expert ophthalmologist. The other half of the data served to evaluate the model of the neuronal network. A 100% correct classification of each eye in the corresponding stage of glaucoma has been achieved. Specificity and sensitivity are 100%. This method provides an efficient and accurate tool for the diagnosis of glaucoma in the stages of glaucomatous illness by means of AI techniques.

[1]  L. Zangwill,et al.  Association between quantitative nerve fiber layer measurement and visual field loss in glaucoma. , 1995, American journal of ophthalmology.

[2]  Malcolm I. Heywood,et al.  Diagnostic Support for Glaucoma Using Retinal Images: A Hybrid Image Analysis and Data Mining Approach , 2005, MIE.

[3]  Anders Heijl,et al.  Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields. , 2005, Investigative ophthalmology & visual science.

[4]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[5]  L Brigatti,et al.  Neural networks to identify glaucoma with structural and functional measurements. , 1996, American journal of ophthalmology.

[6]  R N Weinreb,et al.  Nerve fiber layer measurements with scanning laser polarimetry in ocular hypertension. , 1997, Archives of ophthalmology.

[7]  L Brigatti,et al.  Automatic detection of glaucomatous visual field progression with neural networks. , 1997, Archives of ophthalmology.

[8]  D. Garway-Heath,et al.  Diagnosing glaucoma progression: current practice and promising technologies , 2006, Current opinion in ophthalmology.

[9]  J Katz,et al.  Risk factors for the development of glaucomatous visual field loss in ocular hypertension. , 1994, Archives of ophthalmology.

[10]  J M Miller,et al.  Measurements of peripapillary nerve fiber layer contour in glaucoma. , 1989, American journal of ophthalmology.

[11]  Q Zhou,et al.  Retinal scanning laser polarimetry and methods to compensate for corneal birefringence. , 2006, Bulletin de la Societe belge d'ophtalmologie.

[12]  T. von Speyr,et al.  Diagnóstico precoz del glaucoma , 1914 .

[13]  H. Lemij,et al.  Measurement by nerve fiber analyzer of retinal nerve fiber layer thickness in normal subjects and patients with ocular hypertension. , 1996, American journal of ophthalmology.

[14]  Rudolf F. Albrecht,et al.  Artificial Neural Nets and Genetic Algorithms , 1995, Springer Vienna.

[15]  L. Zangwill,et al.  Scanning laser polarimetry to measure the nerve fiber layer of normal and glaucomatous eyes. , 1995, American journal of ophthalmology.

[16]  Gustavo Santos-García,et al.  The Hopfield and Hamming Networks Applied to the Automatic Speech Recognition of the Five Spanish Vowels , 1993 .

[17]  Gadi Wollstein,et al.  Imaging in glaucoma. , 1996, Ophthalmology clinics of North America.

[18]  C. Glymour,et al.  Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study. , 2005, Investigative ophthalmology & visual science.

[19]  Mei-Ling Huang,et al.  Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography. , 2005, Investigative ophthalmology & visual science.

[20]  Anders Heijl,et al.  Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms , 2007, Journal of glaucoma.

[21]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[22]  William H Swanson,et al.  Evaluation of a Two-Stage Neural Model of Glaucomatous Defect: An Approach to Reduce Test–Retest Variability , 2006, Optometry and vision science : official publication of the American Academy of Optometry.