Show simple item record

dc.contributor.authorKeel, S
dc.contributor.authorLee, PY
dc.contributor.authorScheetz, J
dc.contributor.authorLi, Z
dc.contributor.authorKotowicz, MA
dc.contributor.authorMacIsaac, RJ
dc.contributor.authorHe, M
dc.date.accessioned2020-12-18T04:19:14Z
dc.date.available2020-12-18T04:19:14Z
dc.date.issued2018-03-12
dc.identifierpii: 10.1038/s41598-018-22612-2
dc.identifier.citationKeel, S., Lee, P. Y., Scheetz, J., Li, Z., Kotowicz, M. A., MacIsaac, R. J. & He, M. (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, 8 (1), https://doi.org/10.1038/s41598-018-22612-2.
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/11343/256143
dc.description.abstractThe 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.
dc.languageEnglish
dc.publisherNATURE PUBLISHING GROUP
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleFeasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study
dc.typeJournal Article
dc.identifier.doi10.1038/s41598-018-22612-2
melbourne.affiliation.departmentCentre for Eye Research Australia (CERA)
melbourne.affiliation.departmentOptometry and Vision Sciences
melbourne.affiliation.departmentMedicine (St Vincent's)
melbourne.affiliation.departmentMedicine, Western Health
melbourne.affiliation.departmentOphthalmology (Eye & Ear Hospital)
melbourne.affiliation.facultyAffiliate
melbourne.affiliation.facultyScience
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titleScientific Reports
melbourne.source.volume8
melbourne.source.issue1
dc.rights.licenseCC BY
melbourne.elementsid1313999
melbourne.contributor.authorHe, Mingguang
melbourne.contributor.authorKeel, Stuart
melbourne.contributor.authorKotowicz, Mark
melbourne.contributor.authorMacIsaac, Richard
melbourne.contributor.authorScheetz, Jane
melbourne.contributor.authorLee, Pei Ying
dc.identifier.eissn2045-2322
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record