Automated AI labelling of optic nerve head enables new insights into cross-ancestry glaucoma risk and genetic discovery in over 280,000 images from the UK Biobank and Canadian Longitudinal Study on Aging

Abstract

Cupping of the optic nerve head, a highly heritable trait, is a hallmark of glaucomatous optic neuropathy. Two key parameters are vertical cup-to-disc ratio (VCDR) and vertical disc diameter (VDD). However, manual assessment often suffers from poor accuracy and is time-intensive. Here, we show convolutional neural network models can accurately estimate VCDR and VDD for 282,100 images from both UK Biobank and an independent study (Canadian Longitudinal Study on Aging), enabling cross-ancestry epidemiological studies and new genetic discovery for these optic nerve head parameters. Using the AI approach we perform a systematic comparison of the distribution of VCDR and VDD, and compare these with intraocular pressure and glaucoma diagnoses across various genetically determined ancestries, which provides an explanation for the high rates of normal tension glaucoma in East Asia. We then used the large number of AI gradings to conduct a more powerful genome-wide association study (GWAS) of optic nerve head parameters. Using the AI based gradings increased estimates of heritability by ~50% for VCDR and VDD. Our GWAS identified more than 200 loci for both VCDR and VDD (double the number of loci from previous studies), uncovers dozens of novel biological pathways, with many of the novel loci also conferring risk for glaucoma.

Association between Cognitive Function and Large Optic Nerve Cupping, Accounting for Cup-Disc-Ratio Genetic Risk Score

“…Lastly, in the time since data collection, more CDR SNPs have been identified. 21 A larger CDR SNP panel might yield different results.…”

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

“…Glaucoma is associated with elevated IOP, whereas the ONH is heavily affected by the biomechanical forces due to elevated IOP and is therefore susceptible to structural damage and the associated functional loss. Han et al trained a convolutional neural network (CNN) model on a large dataset of 282,100 images from both the UK Biobank and the Canadian Longitudinal Study on Aging -CLSA for automatic AI labeling of the ONH (15). Their study was able to extract two key ONH parameters: vertical cup-to-disc ratio and vertical disc diameter.…”

“…In another study, a genome-wide meta-analysis identified 127 open-angle glaucoma loci (23). AI-driven algorithms can also be used for assigning vertical cup-to-disc ratios to extend our knowledge about the genetic architecture of glaucoma (15). Such findings may guide the development of novel transgenic mouse models that are highly relevant to the human disease process.…”