Pearse A. Keane

Deep learning in ophthalmology: The technical and clinical considerations.

Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed.

Optical coherence tomography angiography: a review of current and future clinical applications.

Further developments in anterior optical coherence tomography angiography have now allowed for evaluation of anterior segment pathology such as glaucoma, ocular surface diseases, corneal vascularisation, and abnormal iris vasculature.

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

AI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test.

Associations with Retinal Pigment Epithelium Thickness Measures in a Large Cohort: Results from the UK Biobank.

We describe novel findings of RPE-BM thickness in normal individuals, a structure that varies with age, ethnicity, refraction, IOP, and smoking.

Performance of DeepSeek-R1 in ophthalmology: an evaluation of clinical decision-making and cost-effectiveness.

DeepSeek-R1 shows strong diagnostic and management performance comparable to OpenAI o1 across ophthalmic subspecialties, while significantly reducing costs. These results support its use as a cost-effective, open-weight alternative to proprietary models.

Evaluating the Impact of Uveitis on Visual Field Progression Using Large-Scale Real-World Data.

Our analysis suggests that VF loss occurs faster in glaucoma patients with uveitis than those without uveitis.

Ellipsoid Zone Change According to Glaucoma-Stage Advancement.

Differentiating glaucoma from chiasmal compression using optical coherence tomography: the macular naso-temporal ratio.

The mNTR can distinguish between chiasmal compression and POAG with high discrimination.