THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand.

Journal: Diabetic medicine : a journal of the British Diabetic Association

Published Date: Sep 27, 2020

Abstract

AIM: 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.

Authors

E Vaghefi

Toku Eyes, Auckland, New Zealand.
S Yang

Neural Engineering Data Consortium, Temple University, Philadelphia, Pennsylvania, USA, {scott.yang, silvia.lopez, meysam, obeid, picone}@temple.edu.
L Xie

From the Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
S Hill

Department of Ophthalmology, Auckland, New Zealand.
O Schmiedel

Auckland Diabetes Centre, Auckland District Health Board.
R Murphy

School of Medicine, Auckland, New Zealand.
D Squirrell

Toku Eyes, Auckland, New Zealand.

Keywords

Adolescent Adult Aged Aged, 80 and over Algorithms Artificial Intelligence Child Diabetes Mellitus Diabetic Retinopathy Female Humans Image Processing, Computer-Assisted Implementation Science Male Mass Screening Middle Aged New Zealand Retina Retrospective Studies Sensitivity and Specificity Triage Young Adult

External Resources

View on PubMed Access via DOI PubMed (32794618)

THEIA™ development, and testing of artificial intelligence-based primary triage of diabetic retinopathy screening images in New Zealand.

Abstract

Authors

Keywords

External Resources

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