dc.contributor.author |
Vaghefi, E |
|
dc.contributor.author |
Yang, S |
|
dc.contributor.author |
Xie, L |
|
dc.contributor.author |
Hill, S |
|
dc.contributor.author |
Schmiedel, O |
|
dc.contributor.author |
Murphy, R |
|
dc.contributor.author |
Squirrell, D |
|
dc.coverage.spatial |
England |
|
dc.date.accessioned |
2022-06-23T04:20:35Z |
|
dc.date.available |
2022-06-23T04:20:35Z |
|
dc.date.issued |
2021-04 |
|
dc.identifier.citation |
(2021). Diabetic Medicine, 38(4), e14386-. |
|
dc.identifier.issn |
0742-3071 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/60114 |
|
dc.description.abstract |
<h4>Aim</h4>To develop and evaluate an artificial intelligence triage system with high sensitivity for detecting referable diabetic retinopathy and maculopathy, while maintaining high specificity for non-referable disease, for clinical implementation within the New Zealand national diabetic retinopathy screening programme.<h4>Methods</h4>The THEIA™ artificial intelligence system for retinopathy and maculopathy screening, was developed at Toku Eyes using routinely collected retinal screening datasets from two of the largest district health boards in Auckland, New Zealand: the Auckland District Health Board and the Counties Manukau District Health Board. All retinal images from consecutive individuals receiving retinal screening between January 2009 and December 2018 were used. Images were labelled as non-sight-threatening, potentially referable or sight-threatening for New Zealand implementation, or as referable (potentially referable + sight-threatening)/non-referable (non-sight-threatening) for global comparison.<h4>Results</h4>Data from 32 354 unique people with diabetes (63 843 when including multiple visits) were available, of which 95-97%, 0.9-2.4% and 1.1-3.1% were categorized as non-sight-threatening, potentially referable and sight-threatening, respectively. Using the referable/non-referable categories, THEIA achieved overall sensitivity of 94% (95% CI 92-95) in the Auckland District Health Board and 95% (95% CI 92-97) in the Counties Manukau District Health Board datasets, while preserving specificity of 63% (95% CI 62-64) for the Auckland District Health Board and 61% (95% CI 60-62) for the Counties Manukau District Health Board. Implementing THEIA into a New Zealand national diabetic screening programme could significantly reduce the manual grading load.<h4>Conclusion</h4>THEIA, an artificial intelligence tool to assist in clinical decision-making, tailored to the needs of the New Zealand national diabetic screening programme, delivered high sensitivity for detecting referable retinopathy within the multi-ethnic New Zealand population with diabetes. |
|
dc.format.medium |
Print-Electronic |
|
dc.language |
eng |
|
dc.publisher |
Wiley |
|
dc.relation.ispartofseries |
Diabetic medicine : a journal of the British Diabetic Association |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by-nc/4.0/ |
|
dc.subject |
Retina |
|
dc.subject |
Humans |
|
dc.subject |
Diabetic Retinopathy |
|
dc.subject |
Diabetes Mellitus |
|
dc.subject |
Mass Screening |
|
dc.subject |
Sensitivity and Specificity |
|
dc.subject |
Retrospective Studies |
|
dc.subject |
Algorithms |
|
dc.subject |
Artificial Intelligence |
|
dc.subject |
Image Processing, Computer-Assisted |
|
dc.subject |
Adolescent |
|
dc.subject |
Adult |
|
dc.subject |
Aged |
|
dc.subject |
Aged, 80 and over |
|
dc.subject |
Middle Aged |
|
dc.subject |
Child |
|
dc.subject |
Triage |
|
dc.subject |
New Zealand |
|
dc.subject |
Female |
|
dc.subject |
Male |
|
dc.subject |
Young Adult |
|
dc.subject |
Implementation Science |
|
dc.subject |
Eye Disease and Disorders of Vision |
|
dc.subject |
Diabetes |
|
dc.subject |
Eye |
|
dc.subject |
Metabolic and endocrine |
|
dc.subject |
Science & Technology |
|
dc.subject |
Life Sciences & Biomedicine |
|
dc.subject |
Endocrinology & Metabolism |
|
dc.subject |
VALIDATION |
|
dc.subject |
PREVALENCE |
|
dc.subject |
1117 Public Health and Health Services |
|
dc.subject |
1103 Clinical Sciences |
|
dc.subject |
1701 Psychology |
|
dc.title |
THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand. |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1111/dme.14386 |
|
pubs.issue |
4 |
|
pubs.begin-page |
e14386 |
|
pubs.volume |
38 |
|
dc.date.updated |
2022-05-04T03:19:29Z |
|
dc.rights.holder |
Copyright: The author |
en |
dc.identifier.pmid |
32794618 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/32794618 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Research Support, Non-U.S. Gov't |
|
pubs.subtype |
research-article |
|
pubs.subtype |
Evaluation Study |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
810457 |
|
pubs.org-id |
Bioengineering Institute |
|
pubs.org-id |
Medical and Health Sciences |
|
pubs.org-id |
Science |
|
pubs.org-id |
School of Computer Science |
|
pubs.org-id |
Science Research |
|
pubs.org-id |
School of Medicine |
|
pubs.org-id |
Medicine Department |
|
pubs.org-id |
ABI Associates |
|
pubs.org-id |
Maurice Wilkins Centre (2010-2014) |
|
pubs.org-id |
Optometry and Vision Science |
|
dc.identifier.eissn |
1464-5491 |
|
pubs.number |
ARTN e14386 |
|
pubs.record-created-at-source-date |
2022-05-04 |
|
pubs.online-publication-date |
2020-09-27 |
|