TY - JOUR
T1 - Facilitating diabetic retinopathy screening using automated retinal image analysis in underresourced settings
AU - Quinn, Nicola
AU - Brazionis, Laima
AU - Zhu, Benjamin
AU - Ryan, Chris
AU - D'Aloisio, Rossella
AU - Lilian Tang, Hongying
AU - Peto, Tunde
AU - Jenkins, Alicia
AU - Centre of Research Excellence in Diabetic Retinopathy Study, TEAMSnet Study Groups
PY - 2021/9
Y1 - 2021/9
N2 - To evaluate an automated retinal image analysis (ARIA) of indigenous retinal fundus images against a human grading comparator for the classification of diabetic retinopathy (DR) status. Indigenous Australian adults with type 2 diabetes (n = 410) from three remote and very remote primary-care services in the Northern Territory, Australia, underwent teleretinal DR screening. A single, central retinal fundus photograph (opportunistic mydriasis) for each eye was later regraded using a single ARIA and a UK human grader and national DR classification system. The sensitivity and specificity of ARIA were assessed relative to the comparator. Proportionate agreement and a Kappa statistic were also computed. Retinal images from 391 and 393 participants were gradable for 'Any DR' by the human grader and ARIA grader, respectively. 'Any DR' was detected by the human grader in 185 (47.3%) participants and by ARIA in 202 (48.6%) participants (agreement =88.0%, Kappa = 0.76,), whereas proliferative DR was detected in 31 (7.9%) and 37 (9.4%) participants (agreement = 98.2%, Kappa = 0.89,), respectively. The ARIA software had 91.4 (95% CI, 86.3-95.0) sensitivity and 85.0 (95% CI, 79.3-89.5) specificity for detecting 'Any DR' and 96.8 (95% CI, 83.3-99.9) sensitivity and 98.3 (95% CI, 96.4-99.4) specificity for detecting proliferative DR. This ARIA software has high sensitivity for detecting 'Any DR', hence could be used as a triage tool for human graders. High sensitivity was also found for detection of proliferative DR by ARIA. Future versions of this ARIA should include maculopathy and referable DR (CSME and/or PDR). Such ARIA software may benefit diabetes care in less-resourced regions.
AB - To evaluate an automated retinal image analysis (ARIA) of indigenous retinal fundus images against a human grading comparator for the classification of diabetic retinopathy (DR) status. Indigenous Australian adults with type 2 diabetes (n = 410) from three remote and very remote primary-care services in the Northern Territory, Australia, underwent teleretinal DR screening. A single, central retinal fundus photograph (opportunistic mydriasis) for each eye was later regraded using a single ARIA and a UK human grader and national DR classification system. The sensitivity and specificity of ARIA were assessed relative to the comparator. Proportionate agreement and a Kappa statistic were also computed. Retinal images from 391 and 393 participants were gradable for 'Any DR' by the human grader and ARIA grader, respectively. 'Any DR' was detected by the human grader in 185 (47.3%) participants and by ARIA in 202 (48.6%) participants (agreement =88.0%, Kappa = 0.76,), whereas proliferative DR was detected in 31 (7.9%) and 37 (9.4%) participants (agreement = 98.2%, Kappa = 0.89,), respectively. The ARIA software had 91.4 (95% CI, 86.3-95.0) sensitivity and 85.0 (95% CI, 79.3-89.5) specificity for detecting 'Any DR' and 96.8 (95% CI, 83.3-99.9) sensitivity and 98.3 (95% CI, 96.4-99.4) specificity for detecting proliferative DR. This ARIA software has high sensitivity for detecting 'Any DR', hence could be used as a triage tool for human graders. High sensitivity was also found for detection of proliferative DR by ARIA. Future versions of this ARIA should include maculopathy and referable DR (CSME and/or PDR). Such ARIA software may benefit diabetes care in less-resourced regions.
KW - Indigenous Australians
KW - automated retinal image analysis
KW - diabetic retinopathy
U2 - 10.1111/dme.14582
DO - 10.1111/dme.14582
M3 - Article
C2 - 33825229
SN - 0742-3071
VL - 38
JO - Diabetic Medicine
JF - Diabetic Medicine
IS - 9
M1 - e14582
ER -