Christopher Mark

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.

Macular and Optic Nerve Head Vessel Density and Progressive Retinal Nerve Fiber Layer Loss in Glaucoma.

Lower baseline macular and optic nerve head (ONH) vessel density are associated with a faster rate of RNFL progression in mild to moderate glaucoma.

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.

Optical Coherence Tomography Angiography Macular Vascular Density Measurements and the Central 10-2 Visual Field in Glaucoma.

Loss of OCT-A macula vessel density is associated with central 10-2 VF defects. Macula vessel density is a clinically relevant parameter that may enhance monitoring of glaucoma suspects and patients.

Association of Macular and Circumpapillary Microvasculature with Visual Field Sensitivity in Advanced Glaucoma.

ONH and macula OCTA VD and thickness are associated with the severity of visual field damage in advanced primary open angle glaucoma.

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.

Progression of Primary Open-Angle Glaucoma in Diabetic and Nondiabetic Patients.

POAG patients with treated type 2 DM, who had no detectable diabetic retinopathy, had significantly slower rates of RNFL thinning compared to those without diagnosed DM.

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.

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.

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.

The proposed CNNmodel improved the estimation of 10-2 VF map based on circumpapillary SD-OCT RNFL thickness measurements.

Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma.

Longitudinal OCTA measurements complement OCT-derived structural metrics for the evaluation of functional VF loss in patients with glaucoma.

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.

A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design.

Our deep learning models were able to accurately estimate both macula GCIPL and ONH RNFL hemiretinal thickness.

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.

Usability and Clinician Acceptance of a Deep Learning-Based Clinical Decision Support Tool for Predicting Glaucomatous Visual Field Progression.

A CDS tool can be designed to present AI model outputs in a useful, trustworthy manner that clinicians are generally willing to integrate into their clinical decision-making.

Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening.

Leveraging novel technologies and advances in telemedicine and AI-based approaches to glaucoma detection show promise for improving our ability to detect moderate and advanced glaucoma in primary care settings and target higher individuals at high…

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.

Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.

Federated Learning presents a promising strategy to overcome current obstacles in developing AI models for glaucoma screening.

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements.

DL models enable the estimation of VF loss from OCTA images with high accuracy.

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.

Deep Learning Estimation of 24-2 Visual Field Map From Optic Nerve Head Optical Coherence Tomography Angiography.

DL models from OCTA images demonstrated high accuracy in estimating 24-2 VF maps by leveraging information from ONH layers.

Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.

The optimised DL model detected glaucoma progression based on longitudinal macular OCTA images showed good performance. With external validation, it could enhance detection of glaucoma progression.

Stereo Photo Measured ONH Shape Predicts Development of POAG in Subjects With Ocular Hypertension.

Methods for identifying objective, quantitative measurements of 3D ONH structure were developed using a large dataset.