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Publications

Validation of neuron activation patterns for artificial intelligence models in oculomics

Retinal BioAge reveals indicators of cardiovascular-kidney-metabolic syndrome in US and UK Populations

Association of retinal image-based, deep learning cardiac BioAge

With telomere length and cardiovascular biomarkers.

The first prospective study of Toku’s CLAiR technology:

Advancing cardiovascular risk assessment through retinal imaging in the Middle Eastern population.

Development and validation of a deep-learning model

To predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets.

Examination of alternative eGFR definitions on the performance of deep learning models

For detection of chronic kidney disease from fundus photographs.

Use of artificial intelligence on retinal images to accurately predict the risk of cardiovascular event (CVD-AI)

Prospective Study

THEIA development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand – In Publication.

THEIA’s Performance Assessed using a cloud platform

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

THEIA used OCTA for diagnosis of AMD

Quantification of optical coherence tomography angiography in age and age-related macular degeneration using vessel density analysis.

THIEA as multi-modal imaging detection of AMD

Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study.

Retrospective Study

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke.

THEIA as a smoking detection tool

Detection of smoking status from retinal images; a Convolutional Neural Network study.

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