Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia
Jane Scheetz(The University of Melbourne), Mingguang He(Hong Kong Polytechnic University), Christopher Gilfillan(Eastern Health), Richard J. MacIsaac, Myra B. McGuinness(The University of Melbourne), Angus Turner(Lions Eye Institute), Rod O’Day(Centre for Eye Research Australia), Stuart Keel(Centre for Eye Research Australia), Zachary Tan(Unknown), Edith E. Holloway(Deakin University), Dilara Koca(The University of Melbourne), Sukhpal S. Sandhu(Centre for Eye Research Australia), Zhuoting Zhu(The Royal Melbourne Hospital)
Cited by 87
Related Papers
Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial
|The Lancet|2019|609
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
|Scientific Reports|2021|279
An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs
|Diabetes Care|2018|264
Genetic and environmental effects on body mass index from infancy to the onset of adulthood: an individual-based pooled analysis of 45 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) study
|American Journal of Clinical Nutrition|2016|243