Edalati Kiumars

Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma.

VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.

Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

Comparison of Ganglion Cell Layer and Ganglion Cell/Inner Plexiform Layer Measures for Detection of Early Glaucoma.

Macular GCL and GCIPL thicknesses are equivalent for identifying early glaucoma with current OCT technology.

Central Macular Topographic and Volumetric Measures: New Biomarkers for Detection of Glaucoma.

Novel macular shape biomarkers detect early glaucoma with clinically relevant performance. Such biomarkers do not depend on intraretinal segmentation accuracy and may be helpful in eyes with suboptimal macular segmentation.

An Artificial Intelligence-Based Prognostic Model for Prediction of Functional Glaucoma Progression From Clinical and Structural Data.

Our newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.

Prediction of visual field progression with serial optic disc photographs using deep learning.

A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.