Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection

Abstract

Purpose To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. Methods One hundred and ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike’s information criterion (AIC), and prediction confidence interval lengths (PIL). Results For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDX-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT * NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAPFDT, and interaction GDx-TSNIT*NAP-FDT consistently provided better discriminating abilities for detecting early, moderate and severe glaucoma than the best single variable models. Conclusions The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDX-TSNIT * NAP-FDT provides the best glaucoma prediction compared to all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared to using GDx or FDT alone.

Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia

“…For instance, over 15 years ago, Shah et al 14 combined imaging measurements (confocal scanning laser ophthalmoscopy, time-domainOCT, and scanning laser polarimetry) with visual function measurements (standard automated perimetry, frequency-doubling technology perimetry, and short-wave automated perimetry) and determined that adding functional information to structural measurements improved classification of healthy and POAG eyes. Subsequently, similar multimodal model improvement has been reported 15–24 …”

“…Subsequently, similar multimodal model improvement has been reported. [15][16][17][18][19][20][21][22][23][24] Despite the improvements in classification performance for healthy versus glaucomatous eyes provided by multimodal models, en face images, and machine learning analyses, there is concern that imaging of the optic disc is not ideal for detecting glaucoma in highly myopic eyes. In particular, the increased axial length (AL) significantly reduces global measured circumpapillary RNFL thickness and results in a different regional arrangement of the retinal nerve fibers.…”

Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma

“…However AIC values are only used for comparison between a set of candidate models and do not suggest the adequacy of the preferred model. 24 Future studies are required to explore the exact nature of these relationships.…”

“…Results were reported as R 2 (coefficient of determination) with differences between the R 2 s calculated using bootstrapping procedures to estimate the 95% confidence intervals (CI) of the difference in coefficients of determination. Akaike's information criterion (AIC) was used to compare the models for goodness of fit, 24 The smaller the AIC, the better the model. Univariable linear regression models were built using visual field MD as the dependent variable and OCT-A parameters, wiVD and cpVD and SD-OCT RNFL thickness and rim area measurements and other and demographic ocular characteristic variables as the independent variables.…”