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Articles by Stuart Keel
Real-world application of a smartphone-based visual acuity test (WHOeyes) with automatic distance calibration
Abstract Background To develop and assess the usability of a smartphone-based visual acuity (VA) test with an automatic distance calibration (ADC) function, the iOS version of WHOeyes. Methods The WHOeyes was an upgraded version with a distinct feature of ADC of an existing validated VA testing app called V@home.
Development of the WHO eye care competency framework - Human Resources for Health
References World Health Organization. World report on vision [Internet]. Vol. 214, World health Organization. 2019. 1–160 p. https://www.who.int/publications/i/item/9789241516570. Accessed 23 Jan 2023. World Health Organization. Global strategy on human resources for health: Workforce 2030 [Internet]. World Health Organization. 2016. https://apps.who.int/iris/bitstream/handle/10665/250368/9789241511131-eng.pdf. Accessed 23 Jan 2023. Yu M, Keel S, Mills A, Müller A.
Toward Universal Eye Health Coverage
Key Points Question What essential evidence-based eye care interventions for common eye conditions should be covered in universal health coverage?
Rising to the challenge: estimates of the magnitude and causes of vision impairment and blindness
The year 2020 marked the end of the global initiative Vision 2020: The Right to Sight launched by WHO in 1999 to intensify activities for the prevention of blindness.
Future burden of vision loss in Australia: Projections from the National Eye Health Survey
Importance Projections of Australia's future burden of vision loss will inform eye health service delivery. Background This study aimed to forecast bilateral vision loss in Australia from 2020 to 2050. Design Population-based survey. Participants Indigenous and non-indigenous Australians (n = 4253) aged ≥50 years from the National Eye Health Survey (NEHS, 2015-2016).
Utilisation and perceptions towards smart device visual acuity assessment in Australia: a mixed methods approach
Utilisation and perceptions towards smart device visual acuity assessment in Australia: a mixed methods approach Stuart Keel1, Jane Scheetz1, Edith Holloway1, Xiaotong Han2, William Yan1, Andreas Mueller1, Mingguang He2,3 1Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia 2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China 3Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic,...
Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Brief Report December 20, 2018 JAMA Ophthalmol. Published online December 20, 2018. doi:10.1001/jamaophthalmol.2018.6035 Key Points Question Can the networks of 2 validated deep learning models for referable diabetic retinopathy and glaucomatous optic neuropathy be reliably visualized?
An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs
An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs Zhixi Li, Stuart Keel, Chi Liu, Yifan He, Wei Meng, Jane Scheetz, Pei Ying Lee, Jonathan Shaw, Daniel Ting, Tien Yin Wong, Hugh Taylor, Robert Chang, Mingguang He Diabetes Care Dec 2018, 41 (12) 2509-2516; DOI: 10.2337/dc18-0147
Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study
This pilot study indicates that our AI-based DR screening model appears to be feasible, accurate and well accepted by patients attending endocrinology outpatient settings. Herein, we describe novel data of the clinical adoption of a deep-learning algorithm for the detection of referable DR within two Australian endocrinology outpatient clinics. Diabetic retinopathy screening strategies have long been trialled in non-ophthalmic settings in Australia with modest success21,22.
Prevalence of Age-Related Macular Degeneration in Australia: The Australian National Eye Health Survey
Question What is the prevalence of age-related macular degeneration in Australian adults? Findings In this population-based national sample of 3098 nonindigenous and 1738 indigenous Australian adults, the prevalence of late age-related macular degeneration was 0.96% in nonindigenous and 0.17% in indigenous Australians. Age-related macular degeneration was attributed as the main cause of vision loss in 11.1% of nonindigenous and 1.1% of indigenous Australians.
Participant referral rate in the National Eye Health Survey (NEHS)
Our data has identified several high risk groups that required ophthalmic referral including older Australians, non-Indigenous men, Indigenous Australians with self-reported diabetes and those residing in very remote populations who may benefit from improvements in the provision and/or uptake of eye health services. Future longitudinal research is warranted to evaluate the feasibility and efficacy of implementing a referral protocol within a population-based research setting.
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