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Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study

Keel, Stuart, Lee, Pei Ying, Scheetz, Jane, Li, Zhixi, Kotowicz, Mark, MacIsaac, Richard J. and He, Mingguang 2018, Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study, Scientific reports, vol. 8, pp. 1-6, doi: 10.1038/s41598-018-22612-2.

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Title Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study
Author(s) Keel, Stuart
Lee, Pei Ying
Scheetz, Jane
Li, Zhixi
Kotowicz, MarkORCID iD for Kotowicz, Mark orcid.org/0000-0002-8094-1411
MacIsaac, Richard J.
He, Mingguang
Journal name Scientific reports
Volume number 8
Article ID 4330
Start page 1
End page 6
Total pages 6
Publisher Nature
Place of publication London, England
Publication date 2018-03-12
ISSN 2045-2322
Summary The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR ( ≥ pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9 minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients.
Language eng
DOI 10.1038/s41598-018-22612-2
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30110170

Document type: Journal Article
Collections: Faculty of Health
School of Medicine
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.