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Automatic Detection of Glaucomatous Visual Field Progression With Neural Networks

Automatic Detection of Glaucomatous Visual Field Progression With Neural Networks Abstract Objective: To evaluate computerized neural networks to determine visual field progression in patients with glaucoma. Methods: Two hundred thirty-three series of Octopus G1 visual fields of 181 patients with glaucoma were collected. Each series was composed of 4 or more reliable visual fields from patients who had previously undergone automated perimetry. The visual fields were independently evaluated in a masked fashion by 3 experienced observers (K.N.-M, M.W., and J.C.) and were judged to show progression based on the agreement of 2 observers. The stable and progressed series were matched for mean defect at baseline. The threshold data were submitted to a back propagation neural network that was trained to classify each series as stable or progressed. Two thirds of the data were used for the training and the remaining one third to test the performance of the network. This was repeated 3 times to classify all of the series (changing the training and test series). Results: Fifty-nine series of visual fields showed progression and 151 were judged stable. Neural network sensitivity was 73% and specificity was 88% (threshold for progression=0.5). The concordance of the neural network with the observers was good (0.50≤κ≥0.64). Conclusions: A neural network can be trained to recognize visual field progression in good concordance with experienced observers. Neural networks may be used to aid the physician in the evaluation of glaucomatous visual field progression. References 1. Werner EB, Petrig B, Krupin T, Bishop K. Variability of automated visual fields in clinically stable glaucoma patients . Invest Ophthalmol Vis Sci . 1989;30:1083-1089. 2. Boeglin RJ, Caprioli J, Zulauf M. Long-term fluctuation of the visual field in glaucoma . Am J Ophthalmol . 1992;113:396-400. 3. Bebie H, Fankhauser F. Statistical program for the analysis of perimetric data . Doc Ophthalmol Proc Ser . 1981;26:9-10. 4. Holmin C, Krakau CET. Regression analysis of the central visual field in chronic glaucoma cases: a follow-up study using automatic perimetry . Acta Ophthalmol . 1982;60:267-274.Crossref 5. Wu DC, Schwartz B, Nagin P. Trend analyses of automated visual fields . Doc Ophthalmol Proc Ser . 1987;49:175-189. 6. Hills JF, Johnson CA. Evaluation of the t test as a method of detecting visual field changes . Ophthalmology . 1988;95:261-266.Crossref 7. Hoskins HD, Magee SD, Drake MV, Kidd MN. Confidence intervals for change in automated visual fields . Br J Ophthalmol . 1988;72:591-597.Crossref 8. Heijl A, Lindgren G, Lindgren A, et al. Extended empirical statistical package for evaluation of single and multiple fields in glaucoma: statpac 2 . In: Mills, RP, Heijl, A, eds. Perimetry Update 1990/1991 . Amsterdam, the Netherlands: Kugler Publications; 1991:303-315. 9. Morgan RK, Feuer WJ, Anderson DR. Statpac 2 glaucoma change probability . Arch Ophthalmol . 1991;109:1690-1692.Crossref 10. Noureddin BN, Poinoosawmy D, Fietzke FW, Hitchings RA. Regression analysis of visual field progression in low-tension glaucoma . Br J Ophthalmol . 1991;75:493-495.Crossref 11. Wild J, Hussey M, Flanagan J, Trope G. Pointwise topographical and longitudinal modeling of the visual field in glaucoma . Invest Ophthalmol Vis Sci . 1993;34:1907-1916. 12. Smith SD, Katz J, Quigley HA. Analysis of progressive change in automated visual fields in glaucoma . Invest Ophthalmol Vis Sci . 1996;37:1419-1428. 13. Brigatti L, Hoffman D, Caprioli J. Neural networks to identify glaucoma with structural and functional measurements . Am J Ophthalmol . 1996;121:511-523. 14. Goldbaum MH, Sample PA, White H, et al. Interpretation of automated perimetry for glaucoma by neural network Invest Ophthalmol Vis Sci . 1994;35:3362-3373. 15. Mutlukan E, Keating D. Visual field interpretation with a personal computer based neural network . Eye . 1994;8:321-323.Crossref 16. Accornero N, Capozza M. OPTONET: neural network for visual field diagnosis . Med Biol Eng Comput . 1995;33:223-226.Crossref 17. Liu X, Cheng G, Wu JX. Identifying the measurement noise in glaucomatous testing: an artificial neural network approach . Artif Intell Med . 1994;6:401-416.Crossref 18. Madsen EM, Yolton RL. Demonstration of a neural network expert system for recognition of glaucomatous visual field changes . Mil Med . 1994;159:553-557. 19. Spenceley SE, Henson DB, Bull DR. Visual field analysis using artificial neural networks . Ophthalmic Physiol Opt . 1994;14:239-248.Crossref 20. Caudill M, Butler C. Naturally Intelligent Systems . Cambridge, Mass: MIT Press; 1990. 21. Hanley JA, McNeil BJ. The meaning and use of the area under the receiver operating characteristic (ROC) curve . Radiology . 1982;143:29-36.Crossref 22. Armitage P, Berry G. Statistical Methods in Medical Research . 3rd ed. Oxford, England: Blackwell Scientific Publications Ltd; 1994:443-447. 23. Landis JR, Koch GG. The measurement of observer agreement for categorical data . Biometrics . 1997;33:159-174.Crossref 24. Fleiss JL. The measurement of interrater agreement . In: Statistical Methods for Rates and Proportions . New York, NY: John Wiley & Sons Inc; 1981:211-236. 25. Werner EB, Bishop KI, Koelle J, et al. A comparison of experienced clinical observers and statistical tests in detection of progressive visual field loss in glaucoma using automated perimeter . Arch Ophthalmol . 1988;106:619-623.Crossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Ophthalmology American Medical Association

Automatic Detection of Glaucomatous Visual Field Progression With Neural Networks

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Publisher
American Medical Association
Copyright
Copyright © 1997 American Medical Association. All Rights Reserved.
ISSN
0003-9950
eISSN
1538-3687
DOI
10.1001/archopht.1997.01100150727005
Publisher site
See Article on Publisher Site

Abstract

Abstract Objective: To evaluate computerized neural networks to determine visual field progression in patients with glaucoma. Methods: Two hundred thirty-three series of Octopus G1 visual fields of 181 patients with glaucoma were collected. Each series was composed of 4 or more reliable visual fields from patients who had previously undergone automated perimetry. The visual fields were independently evaluated in a masked fashion by 3 experienced observers (K.N.-M, M.W., and J.C.) and were judged to show progression based on the agreement of 2 observers. The stable and progressed series were matched for mean defect at baseline. The threshold data were submitted to a back propagation neural network that was trained to classify each series as stable or progressed. Two thirds of the data were used for the training and the remaining one third to test the performance of the network. This was repeated 3 times to classify all of the series (changing the training and test series). Results: Fifty-nine series of visual fields showed progression and 151 were judged stable. Neural network sensitivity was 73% and specificity was 88% (threshold for progression=0.5). The concordance of the neural network with the observers was good (0.50≤κ≥0.64). Conclusions: A neural network can be trained to recognize visual field progression in good concordance with experienced observers. Neural networks may be used to aid the physician in the evaluation of glaucomatous visual field progression. References 1. Werner EB, Petrig B, Krupin T, Bishop K. Variability of automated visual fields in clinically stable glaucoma patients . Invest Ophthalmol Vis Sci . 1989;30:1083-1089. 2. Boeglin RJ, Caprioli J, Zulauf M. Long-term fluctuation of the visual field in glaucoma . Am J Ophthalmol . 1992;113:396-400. 3. Bebie H, Fankhauser F. Statistical program for the analysis of perimetric data . Doc Ophthalmol Proc Ser . 1981;26:9-10. 4. Holmin C, Krakau CET. Regression analysis of the central visual field in chronic glaucoma cases: a follow-up study using automatic perimetry . Acta Ophthalmol . 1982;60:267-274.Crossref 5. Wu DC, Schwartz B, Nagin P. Trend analyses of automated visual fields . Doc Ophthalmol Proc Ser . 1987;49:175-189. 6. Hills JF, Johnson CA. Evaluation of the t test as a method of detecting visual field changes . Ophthalmology . 1988;95:261-266.Crossref 7. Hoskins HD, Magee SD, Drake MV, Kidd MN. Confidence intervals for change in automated visual fields . Br J Ophthalmol . 1988;72:591-597.Crossref 8. Heijl A, Lindgren G, Lindgren A, et al. Extended empirical statistical package for evaluation of single and multiple fields in glaucoma: statpac 2 . In: Mills, RP, Heijl, A, eds. Perimetry Update 1990/1991 . Amsterdam, the Netherlands: Kugler Publications; 1991:303-315. 9. Morgan RK, Feuer WJ, Anderson DR. Statpac 2 glaucoma change probability . Arch Ophthalmol . 1991;109:1690-1692.Crossref 10. Noureddin BN, Poinoosawmy D, Fietzke FW, Hitchings RA. Regression analysis of visual field progression in low-tension glaucoma . Br J Ophthalmol . 1991;75:493-495.Crossref 11. Wild J, Hussey M, Flanagan J, Trope G. Pointwise topographical and longitudinal modeling of the visual field in glaucoma . Invest Ophthalmol Vis Sci . 1993;34:1907-1916. 12. Smith SD, Katz J, Quigley HA. Analysis of progressive change in automated visual fields in glaucoma . Invest Ophthalmol Vis Sci . 1996;37:1419-1428. 13. Brigatti L, Hoffman D, Caprioli J. Neural networks to identify glaucoma with structural and functional measurements . Am J Ophthalmol . 1996;121:511-523. 14. Goldbaum MH, Sample PA, White H, et al. Interpretation of automated perimetry for glaucoma by neural network Invest Ophthalmol Vis Sci . 1994;35:3362-3373. 15. Mutlukan E, Keating D. Visual field interpretation with a personal computer based neural network . Eye . 1994;8:321-323.Crossref 16. Accornero N, Capozza M. OPTONET: neural network for visual field diagnosis . Med Biol Eng Comput . 1995;33:223-226.Crossref 17. Liu X, Cheng G, Wu JX. Identifying the measurement noise in glaucomatous testing: an artificial neural network approach . Artif Intell Med . 1994;6:401-416.Crossref 18. Madsen EM, Yolton RL. Demonstration of a neural network expert system for recognition of glaucomatous visual field changes . Mil Med . 1994;159:553-557. 19. Spenceley SE, Henson DB, Bull DR. Visual field analysis using artificial neural networks . Ophthalmic Physiol Opt . 1994;14:239-248.Crossref 20. Caudill M, Butler C. Naturally Intelligent Systems . Cambridge, Mass: MIT Press; 1990. 21. Hanley JA, McNeil BJ. The meaning and use of the area under the receiver operating characteristic (ROC) curve . Radiology . 1982;143:29-36.Crossref 22. Armitage P, Berry G. Statistical Methods in Medical Research . 3rd ed. Oxford, England: Blackwell Scientific Publications Ltd; 1994:443-447. 23. Landis JR, Koch GG. The measurement of observer agreement for categorical data . Biometrics . 1997;33:159-174.Crossref 24. Fleiss JL. The measurement of interrater agreement . In: Statistical Methods for Rates and Proportions . New York, NY: John Wiley & Sons Inc; 1981:211-236. 25. Werner EB, Bishop KI, Koelle J, et al. A comparison of experienced clinical observers and statistical tests in detection of progressive visual field loss in glaucoma using automated perimeter . Arch Ophthalmol . 1988;106:619-623.Crossref

Journal

Archives of OphthalmologyAmerican Medical Association

Published: Jun 1, 1997

References