Goldbaum Michael H

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Deep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images.

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

A computational approach can identify structural features that improve glaucoma detection and progression prediction.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

Deep learning glaucoma detection can achieve high accuracy across diverse datasets with appropriate training strategies.

Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.

Deep learning en face image analysis improves on feature-based GBC models for classifying healthy and glaucoma eyes.

Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

The model's high diagnostic accuracy using OHTS photographs suggests that DL has the potential to standardize and automate POAG determination for clinical trials and management.

Gradient-Boosting Classifiers Combining Vessel Density and Tissue Thickness Measurements for Classifying Early to Moderate Glaucoma.

GBCs that combine OCTA and OCT macula and ONH measurements can improve diagnostic accuracy for glaucoma detection compared to most but not all instrument provided parameters.

Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT.

Deep learning models improved estimates of functional loss from SD OCT imaging. Accurate estimates can help clinicians to individualize VF testing to patients.

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

Eye-specific ROIs identified using DL-AE analysis of OCT images show promise for improving assessment of glaucomatous progression.

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information.