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.
