CNN-Based Device-Agnostic Feature Extraction From ONH OCT Scans.

Purpose: Optical coherence tomography (OCT)-derived measurements of the optic nerve head (ONH) from different devices are not interchangeable. This poses challenges to patient follow-up and collaborative studies. Here, we present a device-agnostic method for the extraction of OCT biomarkers using artificial intelligence.

Effect of ethnic diversity on the saccadic reaction time among healthy Indian and Dutch adults.

Eye movement perimetry (EMP) expresses the decline in visual field (VF) responsiveness based on the deviation in saccadic reaction times (SRTs) from their expected age-similar responses (normative database). Since ethnic dissimilarities tend to affect saccade parameters, we evaluated the effect of such a factor on SRT and its interaction with age, stimulus eccentricity, and intensity. 149 healthy adults, spread into five age groups, drawn from Indian and Dutch ethnicities underwent a customized EMP protocol integrated with a saccade task from which the SRTs to 'seen' visual stimuli were computed.

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.

Purpose: To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements.

Design: Algorithm development for RNFL damage severity classification based on multicenter OCT data.

Subjects And Participants: A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images.

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs.

Purpose: Significant visual impairment due to glaucoma is largely caused by the disease being detected too late.

Objective: To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG).

Design: Cross-sectional study.