THEIA™ development, and testing of artificial intelligence‐based primary triage of diabetic retinopathy screening images in New Zealand-Reference-Cited by-同舟云学术

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THEIA™ development, and testing of artificial intelligence‐based primary triage of diabetic retinopathy screening images in New Zealand

Published:2020-09-27 Issue:4 Volume:38 Page:
ISSN:0742-3071
Container-title:Diabetic Medicine
language:en
Short-container-title:Diabet Med

Author:

Vaghefi E.¹²^ORCID,Yang S.²³,Xie L.²,Hill S.⁴,Schmiedel O.⁵,Murphy R.⁶,Squirrell D.¹⁴

Affiliation:

1. Toku Eyes Auckland New Zealand

2. School of Optometry and Vision Science Auckland New Zealand

3. School of Computer Sciences University of Auckland Auckland New Zealand

4. Department of Ophthalmology Auckland New Zealand

5. Auckland Diabetes Centre Auckland District Health Board

6. School of Medicine Auckland New Zealand

Publisher

Wiley

Subject

Endocrinology,Endocrinology, Diabetes and Metabolism,Internal Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/dme.14386

Reference31 articles.

1. Automated Screening for Diabetic Retinopathy – A Systematic Review

2. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review

3. Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application

4. Machine Learning Has Arrived!

5. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

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1. Detection of diabetic retinopathy using artificial intelligence: an exploratory systematic review;LatIA;2024-09-02

2. Retinal BioAge Reveals Indicators of Cardiovascular-Kidney-Metabolic Syndrome in US and UK Populations;2024-07-19

3. 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;Cardiovascular Digital Health Journal;2024-04

4. Prevalence of diabetic retinopathy assessed using two-field mydriatic fundus photography;Russian Annals of Ophthalmology;2024

5. Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs;PLOS ONE;2023-11-30

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