Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points

Yousefi, Siamak; Goldbaum, Michael H.; Balasubramanian, Madhusudhanan; Jung, Tzyy-Ping; Weinreb, Robert N.; Medeiros, Felipe A.; Zangwill, Linda M.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Bowd, Christopher

doi:10.1109/tbme.2013.2295605

Cited by 97 publications

(61 citation statements)

References 40 publications

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“…Recent advances in machine learning techniques provide new approaches for glaucoma-related diagnosis and progression detection based on learning from a pool of data [11–15]. 6) Variational Bayesian independent component analysis mixture model (VIM) for glaucoma defect pattern identification and progression detection is a machine learning classification-based approach developed by our group [16–18].…”

Section: Introductionmentioning

confidence: 99%

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Yousefi

Goldbaum

Varnousfaderani

et al. 2015

Journal of Biomedical Informatics

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Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using standard automated perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient’s eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants.

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Section: Introductionmentioning

confidence: 99%

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Yousefi

Goldbaum

Varnousfaderani

et al. 2015

Journal of Biomedical Informatics

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“…Machine learning techniques have been widely used in biomedical applications [1]–[14]. Recent advances in data analysis and a significant growth in available database size have promoted classification methods that are capable of identifying previously hidden clusters and patterns in available datasets.…”

Section: Introductionmentioning

confidence: 99%

Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Yousefi

Goldbaum

Balasubramanian

et al. 2014

IEEE Trans. Biomed. Eng.

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A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods.

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“…Pattern classification is a broad subject with applications in many different fields including biomedicine [1–9]. Recognizing patterns hidden in data sets can result in extracting valuable knowledge about the data.…”

Section: Introductionmentioning

confidence: 99%

Recognizing patterns of visual field loss using unsupervised machine learning

et al. 2014

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Glaucoma is a potentially blinding optic neuropathy that results in a decrease in visual sensitivity. Visual field abnormalities (decreased visual sensitivity on psychophysical tests) are the primary means of glaucoma diagnosis. One form of visual field testing is Frequency Doubling Technology (FDT) that tests sensitivity at 52 points within the visual field. Like other psychophysical tests used in clinical practice, FDT results yield specific patterns of defect indicative of the disease. We used Gaussian Mixture Model with Expectation Maximization (GEM), (EM is used to estimate the model parameters) to automatically separate FDT data into clusters of normal and abnormal eyes. Principal component analysis (PCA) was used to decompose each cluster into different axes (patterns). FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal (i.e., glaucomatous) FDT results, recruited from a university-based, longitudinal, multi-center, clinical study on glaucoma. The GEM input was the 52-point FDT threshold sensitivities for all eyes. The optimal GEM model separated the FDT fields into 3 clusters. Cluster 1 contained 94% normal fields (94% specificity) and clusters 2 and 3 combined, contained 77% abnormal fields (77% sensitivity). For clusters 1, 2 and 3 the optimal number of PCA-identified axes were 2, 2 and 5, respectively. GEM with PCA successfully separated FDT fields from healthy and glaucoma eyes and identified familiar glaucomatous patterns of loss.

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Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points

Cited by 97 publications

References 40 publications

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements

Recognizing patterns of visual field loss using unsupervised machine learning

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