Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

PURPOSE To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. METHODS In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.24 +/- 2.65 years and 8.11 +/- 2.42 visual fields were evaluated with the vB-ICA-mm. In addition, eyes with progressive GON (PGON) were identified (n = 39). Each participant had a series of fields tested, with each field entered independently and placed along the axes of the previously developed model. This allowed change in one pattern of visual field defect (along one axis) to be assessed relative to results other areas of that same field (no change along other axes). Progression was based on a slope falling outside the 5th and the 95th percentile limits of all slopes, with at least two axes not showing such a deviation in a given individual's series of fields. Fields were also scored using Advanced Glaucoma Intervention Study (AGIS) and the Early Manifest Glaucoma Treatment Trial (EMGT) criteria. RESULTS Thirty-two of 191 eyes progressed on vB-ICA-mm by this definition. Of the 32, 22 had field loss at baseline, 7 had only GON, 3 were OHTs and 12 were from the 39 eyes (31%) with PGON. The vB-ICA-mm identified a higher percentage of progressing eyes in each diagnostic category than did AGIS or and the EMGT. CONCLUSIONS The vB-ICA-mm can quantitatively identify progression in eyes with glaucoma by evaluating change in one or more patterns of the visual field loss while other areas or patterns remain stable. This may enable each eye to contribute to the determination of whether change is caused by true progression or by variability.

[1]  Robert N Weinreb,et al.  Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. , 2005, Investigative ophthalmology & visual science.

[2]  Robert N Weinreb,et al.  Patterns of glaucomatous visual field progression identified by three progression criteria. , 2004, American journal of ophthalmology.

[3]  Gang Li,et al.  Predictive factors for glaucomatous visual field progression in the Advanced Glaucoma Intervention Study. , 2004, Ophthalmology.

[4]  J. Katz,et al.  The ocular hypertension treatment study: intraocular pressure lowering prevents the development of glaucoma, but does that mean we should treat before the onset of disease? , 2004, Archives of ophthalmology.

[5]  J. Beiser,et al.  The Ocular Hypertension Treatment Study: topical medication delays or prevents primary open-angle glaucoma in African American individuals. , 2004, Archives of ophthalmology.

[6]  Chris A. Johnson,et al.  Comparison of different methods for detecting glaucomatous visual field progression. , 2003, Investigative ophthalmology & visual science.

[7]  Anders Heijl,et al.  Factors for glaucoma progression and the effect of treatment: the early manifest glaucoma trial. , 2003, Archives of ophthalmology.

[8]  M. C. Leske,et al.  Reduction of intraocular pressure and glaucoma progression: results from the Early Manifest Glaucoma Trial. , 2002, Archives of ophthalmology.

[9]  L. Zangwill,et al.  Infrequent confirmation of visual field progression. , 2002, Ophthalmology.

[10]  E. E. Hartmann,et al.  The Ocular Hypertension Treatment Study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. , 2002, Archives of ophthalmology.

[11]  Chris A Johnson,et al.  Identification of progressive glaucomatous visual field loss. , 2002, Survey of ophthalmology.

[12]  B. Prum,et al.  The advanced glaucoma intervention study (AGIS): 7. the relationship between control of intraocular pressure and visual field deterioration , 2000 .

[13]  M. Kass,et al.  Intraocular pressure and visual field progression in open-angle glaucoma. , 2000, American journal of ophthalmology.

[14]  N. Congdon,et al.  Methodological variations in estimating apparent progressive visual field loss in clinical trials of glaucoma treatment , 2000 .

[15]  P. Lichter,et al.  The Collaborative Initial Glaucoma Treatment Study: study design, methods, and baseline characteristics of enrolled patients. , 1999, Ophthalmology.

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

[17]  Susan E. George,et al.  Artificial neural network analysis of noisy visual field data in glaucoma , 1997, Artif. Intell. Medicine.

[18]  D B Henson,et al.  Spatial classification of glaucomatous visual field loss. , 1996, The British journal of ophthalmology.

[19]  P. Wishart,et al.  Determining progressive visual field loss in serial Humphrey visual fields. , 1995, Ophthalmology.

[20]  J M Wild,et al.  Long‐term follow‐up of baseline learning and fatigue effects in the automated perimetry of glaucoma and ocular hypertensive patients , 1991, Acta ophthalmologica.

[21]  S M Drance,et al.  The mode of progression of visual field defects in glaucoma. , 1984, American journal of ophthalmology.

[22]  J Flammer,et al.  Differential light threshold. Short- and long-term fluctuation in patients with glaucoma, normal controls, and patients with suspected glaucoma. , 1984, A M A Archives of Ophthalmology.