Brye Nicole

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.

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.

Comparison of the Effects of Latanoprostene Bunod and Timolol on Retinal Blood Vessel Density: A Randomized Clinical Trial.

Topical administration of LBN enhanced macular VD in patients with OAG or patients with OHT. In contrast, timolol administration did not have any effect on VD.

Effect of Testing Frequency on the Time to Detect Glaucoma Progression With Optical Coherence Tomography (OCT) and OCT Angiography.

The time required to detect a statistically significant negative cpRNFL and cpCD slope decreased as the testing frequency increased, albeit not proportionally.

Time to Glaucoma Progression Detection by Optical Coherence Tomography in Individuals of African and European Descents.

Time to progression detection was similar for both races when assuming identical RNFLT baseline/thinning rates, and shorter in AD eyes under real-world simulation when AD had faster RNFLT thinning.

Number of macula optical coherence tomography scans needed to detect glaucoma progression.

Increasing the frequency of macular OCT testing to three times per year more sensitively detects progression compared with two times per year.