Biomaterial Database

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis. 2009

Michael H Goldbaum, and Gil-Jin Jang, and Chris Bowd, and Jiucang Hao, and Linda M Zangwill, and Jeffrey Liebmann, and Christopher Girkin, and Tzyy-Ping Jung, and Robert N Weinreb, and Pamela A Sample

Department of Ophthalmology, University of California San Diego, USA.

OBJECTIVE To determine if the patterns uncovered with variational Bayesian-independent component analysis-mixture model (VIM) applied to a large set of normal and glaucomatous fields obtained with the Swedish Interactive Thresholding Algorithm (SITA) are distinct, recognizable, and useful for modeling the severity of the field loss. METHODS SITA fields were obtained with the Humphrey Visual Field Analyzer (Carl Zeiss Meditec, Inc, Dublin, California) on 1,146 normal eyes and 939 glaucoma eyes from subjects followed by the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. VIM modifies independent component analysis (ICA) to develop separate sets of ICA axes in the cluster of normal fields and the 2 clusters of abnormal fields. Of 360 models, the model with the best separation of normal and glaucomatous fields was chosen for creating the maximally independent axes. Grayscale displays of fields generated by VIM on each axis were compared. SITA fields most closely associated with each axis and displayed in grayscale were evaluated for consistency of pattern at all severities. RESULTS The best VIM model had 3 clusters. Cluster 1 (1,193) was mostly normal (1,089, 95% specificity) and had 2 axes. Cluster 2 (596) contained mildly abnormal fields (513) and 2 axes; cluster 3 (323) held mostly moderately to severely abnormal fields (322) and 5 axes. Sensitivity for clusters 2 and 3 combined was 88.9%. The VIM-generated field patterns differed from each other and resembled glaucomatous defects (eg, nasal step, arcuate, temporal wedge). SITA fields assigned to an axis resembled each other and the VIM-generated patterns for that axis. Pattern severity increased in the positive direction of each axis by expansion or deepening of the axis pattern. CONCLUSIONS VIM worked well on SITA fields, separating them into distinctly different yet recognizable patterns of glaucomatous field defects. The axis and pattern properties make VIM a good candidate as a preliminary process for detecting progression.

UI	MeSH Term	Description	Entries
D003941	Diagnostic Techniques, Ophthalmological	Methods and procedures for the diagnosis of diseases of the eye or of vision disorders.	Diagnostic Technic, Ophthalmological,Diagnostic Technics, Ophthalmologic,Diagnostic Technics, Ophthalmological,Diagnostic Technique, Ophthalmological,Diagnostic Techniques, Ophthalmologic,Ophthalmological Diagnostic Technic,Ophthalmological Diagnostic Technics,Ophthalmological Diagnostic Technique,Ophthalmological Diagnostic Techniques,Technic, Ophthalmological Diagnostic,Technics, Ophthalmological Diagnostic,Technique, Ophthalmological Diagnostic,Techniques, Ophthalmological Diagnostic,Diagnostic Technic, Ophthalmologic,Diagnostic Technique, Ophthalmologic,Ophthalmologic Diagnostic Technic,Ophthalmologic Diagnostic Technics,Ophthalmologic Diagnostic Technique,Ophthalmologic Diagnostic Techniques,Technic, Ophthalmologic Diagnostic,Technics, Ophthalmologic Diagnostic,Technique, Ophthalmologic Diagnostic,Techniques, Ophthalmologic Diagnostic
D005901	Glaucoma	An ocular disease, occurring in many forms, having as its primary characteristics an unstable or a sustained increase in the intraocular pressure which the eye cannot withstand without damage to its structure or impairment of its function. The consequences of the increased pressure may be manifested in a variety of symptoms, depending upon type and severity, such as excavation of the optic disk, hardness of the eyeball, corneal anesthesia, reduced visual acuity, seeing of colored halos around lights, disturbed dark adaptation, visual field defects, and headaches. (Dictionary of Visual Science, 4th ed)	Glaucomas
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D012680	Sensitivity and Specificity	Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)	Specificity,Sensitivity,Specificity and Sensitivity
D012720	Severity of Illness Index	Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.	Illness Index Severities,Illness Index Severity
D014794	Visual Fields	The total area or space visible in a person's peripheral vision with the eye looking straightforward.	Field, Visual,Fields, Visual,Visual Field
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model