Publications by authors named "R Linderman"
Purpose: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. Here, we explore 12 distinct AI architecture combinations to determine the most effective approach for GA segmentation.
View Article and Find Full Text PDFPurpose: To gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy (GA) with fundus autofluorescence (FAF) images.
Design: Evaluation of artificial intelligence (AI) algorithms.
Subjects: The Age-Related Eye Disease Study 2 (AREDS2) images were used for training and cross-validation, and GA clinical trial images were used for testing.
Article Synopsis
- The study aimed to analyze how sensitive different quantitative metrics of retinal blood vessels are when using optical coherence tomography angiography (OCT-A) images.
- Full retinal images from 94 healthy individuals were used to simulate capillary loss, with various metrics calculated to assess sensitivity to this loss.
- Results showed that commonly used metrics like foveal avascular zone (FAZ) area and vessel density were not effective at detecting capillary loss until significant loss occurred, highlighting the need for further research to understand different loss patterns and improve metric reliability.
Quantitative assessment of OCT-A images includes evaluating circularity and roundness of the FAZ. Inconsistent or inaccurate mathematical definitions of these metrics impacts their utility as biomarkers and impairs the ability to combine and compare results across studies.
View Article and Find Full Text PDFPurpose: To assess the impact of two hypomorphic alleles (R402Q and S192Y) on foveal pit and foveal avascular zone (FAZ) morphology.
Design: Prospective, cross-sectional study.
Participants: A total of 164 participants with normal vision (67 male and 97 female; mean ± standard deviation [SD] age = 30.