Belghith Akram

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

Decreased vessel density was significantly associated with the severity of visual field damage independent of the structural loss.

Deep Retinal Layer Microvasculature Dropout Detected by the Optical Coherence Tomography Angiography in Glaucoma.

Systemic and ocular factors including focal LC defects more advanced glaucoma, reduced RNFL vessel density, thinner choroidal thickness, and lower diastolic blood pressure were factors associated with the parapapillary deep-layer microvasculature dropout in glaucomatous eyes.

Estimating Optical Coherence Tomography Structural Measurement Floors to Improve Detection of Progression in Advanced Glaucoma.

In advanced glaucoma, more GC-IPL tissue remains above the measurement floor compared with other measurements, suggesting GC-IPL thickness is the better candidate for detecting progression. Progression in SDOCT measurements is observable in advanced disease.

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.

Optical Coherence Tomography Angiography Vessel Density in Glaucomatous Eyes with Focal Lamina Cribrosa Defects.

In eyes with similar severity of glaucoma, OCT-A-measured vessel density was significantly lower in POAG eyes with focal LC defects than in eyes without an LC defect.

Reproducibility of Optical Coherence Tomography Angiography Macular and Optic Nerve Head Vascular Density in Glaucoma and Healthy Eyes.

Reproducibility of OCT-A ONH and macula vessel density measurements is good. Moreover, glaucoma patients have sparser vessel density with poorer reproducibility than healthy subjects.

Comparing the Rates of Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Loss in Healthy Eyes and in Glaucoma Eyes.

In this cohort, the rate of circumpapillary RNFL thickness change was faster than macular GCIPL change for glaucoma eyes.

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-Layer Microvasculature Dropout by Optical Coherence Tomography Angiography and Microstructure of Parapapillary Atrophy.

Parapapillary deep-layer microvasculature dropout was associated with the presence and larger width of γPPA, but not with the βPPA+BM width.

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.

Racial Differences in Rate of Change of Spectral-Domain Optical Coherence Tomography-Measured Minimum Rim Width and Retinal Nerve Fiber Layer Thickness.

Race is an important consideration when assessing structural change, particularly minimum rim width, in glaucoma suspect eyes. Differences in rate of structural change may help explain racial disparities in glaucoma susceptibility.

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.

Automated Beta Zone Parapapillary Area Measurement to Differentiate Between Healthy and Glaucoma Eyes.

Larger βPPA area, as determined by automated OCT assessment, is significantly associated with a diagnosis of glaucoma, even after adjusting for age and AL, and may aid in differentiating healthy from glaucomatous eyes.

A Longitudinal Analysis of Peripapillary Choroidal Thinning in Healthy and Glaucoma Subjects.

The rate of peripapillary choroidal thinning was not significantly different between healthy and glaucoma eyes during this relatively short follow-up period.

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.

Relationship of Corneal Hysteresis and Anterior Lamina Cribrosa Displacement in Glaucoma.

Lower corneal hysteresis was significantly associated with posterior displacement of the anterior lamina cribrosa over time. These data provide additional support for lower corneal hysteresis being a risk factor for glaucoma progression.

Racial Differences in the Association of Anterior Lamina Cribrosa Surface Depth and Glaucoma Severity in the African Descent and Glaucoma Evaluation Study (ADAGES).

This study demonstrates that a deeper ALCSD, regardless of the ALCSD reference plane used, is associated with more severe glaucoma and higher IOP in the ADAGES cohort, particularly in individuals of AD.

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model.

Estimated Utility of the Short-term Assessment of Glaucoma Progression Model in Clinical Practice.

In this cohort study, results from the STAGE model with reduction of the rate of progression as the end point, frequent testing, and a moderate effect size, suggest that clinical trials to test efficacy of…

Bruch Membrane Opening Detection Accuracy in Healthy Eyes and Eyes With Glaucoma With and Without Axial High Myopia in an American and Korean Cohort.

As BMO location inaccuracy was 2.4 times more likely in eyes with high axial myopia regardless of diagnosis, optical coherence tomography images of high myopes should be reviewed carefully, and when possible, BMO location should…

Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.

The current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes.

Diagnostic Accuracy of Optic Nerve Head and Macula OCT Parameters for Detecting Glaucoma in Eyes With and Without High Axial Myopia.

The diagnostic accuracy for pRNFL and GCIPL was high for high axial myopic eyes and shows promise for glaucoma detection in high myopes.

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.

Combining OCT-based RNFL thickness maps with texture-based en face images showed a better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.

Comparing optical coherence tomography radial and cube scan patterns for measuring Bruch's membrane opening minimum rim width (BMO-MRW) in glaucoma and healthy eyes: cross-sectional and longitudinal analysis.

Although the cube scan-based BMO-MRW was significantly smaller than the radial scan-based BMO-MRW, we found no significant difference between the two scan patterns for detecting glaucoma, identifying BMO location and measuring the rate of BMO-MRW change.

Racial Differences in the Rate of Change in Anterior Lamina Cribrosa Surface Depth in the African Descent and Glaucoma Evaluation Study.

Glaucomatous remodeling of the lamina cribrosa differs between AD and ED patients with glaucoma.

Performance of General-Purpose Vision Language Models and Ophthalmology Foundation Models in Glaucoma Detection and Function Prediction.

Fine-tuned VLMs demonstrated high performance in glaucoma detection and VF MD prediction, matching or exceeding specialized foundation models and traditional convolutional neural network (CNN)-based methods.

Relationship of 24-2C Central Visual Field Damage to Juxtapapillary Choriocapillaris Dropout in Glaucoma Eyes With or Without Axial Myopia.

MvD area and angular circumference are significantly associated with central VF damage detected by VF 24-2C in POAG eyes with and without axial myopia.

Diagnostic Accuracy of 3D Deep Learning Classifiers for Glaucoma Detection: A Comparison of Cross-Domain and Device-Specific Models.

The 3D DL classifier showed significantly higher diagnostic accuracy than global GCIPL thickness but was similar in performance to the 3D CD-DL classifier.

Rates of Choriocapillaris Microvascular Dropout and Macular Structural Changes in Glaucomatous Optic Neuropathy With and Without Myopia.

Rates of GCIPL thinning were associated with rates of MvD area and angular circumference change over time in myopic POAG eyes.

Wide-Field Optical Coherence Tomography Imaging Improves Rate of Change Detection in Progressing Glaucomatous Eyes Compared With Standard-Field Imaging.

In this cohort that includes eyes with and without high axial myopia, the mean rate of retinal nerve fiber layer thinning measured using SWF images was faster in eyes with progressing glaucoma than in eyes with nonprogressing glaucoma.

Structural Change Can Be Detected in Advanced-Glaucoma Eyes.

Ganglion cell-inner plexiform layer and 3D volume BKDS show promise for identifying change in severely advanced glaucoma.

Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

Optical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes.

Rate and Pattern of Rim Area Loss in Healthy and Progressing Glaucoma Eyes.

Compared with healthy eyes, the mean rate of global rim area loss was 3.7 times faster and the mean rate of global percentage rim area loss was 5.4 times faster in progressing glaucoma eyes.

Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA).

Bruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression.