Introduction: The purpose of this study was to compare 2-field (2F) and 5-field (5F) mydriatic handheld retinal imaging for the assessment of diabetic retinopathy (DR) severity in a community-based DR screening program (DRSP). Methods: This was a prospective, cross-sectional diagnostic study, evaluating images of 805 eyes from 407 consecutive patients with diabetes acquired from a community-based DRSP. Mydriatic standardized 5F imaging (macula, disc, superior, inferior, temporal) with handheld retinal camera was performed. 2F (disc, macula), and 5F images were independently assessed using the International DR classification at a centralized reading center. Simple (K) and weighted (Kw) kappa statistics were calculated for DR. Sensitivity and specificity for referable DR ([refDR] moderate nonproliferative DR [NPDR] or worse) and vision-threatening DR ([vtDR] severe NPDR or worse) for 2F compared to 5F imaging were calculated. Results: Distribution of DR severity by 2F/5F images (%): no DR 66.0/61.7, mild NPDR 10.7/14.4, moderate NPDR 7.9/8.1, severe NPDR 3.3/5.6, proliferative DR 5.6/4.6, ungradable 6.5/5.6. Exact agreement of DR grading between 2F and 5F was 81.7%, within 1-step 97.1% (K = 0.64, Kw = 0.78). Sensitivity/specificity for 2F compared 5F was refDR 0.80/0.97, vtDR 0.73/0.98. The ungradable images rate with 2F was 16.1% higher than with 5F (6.5 vs. 5.6%, p < 0.001). Conclusions: Mydriatic 2F and 5F handheld imaging have substantial agreement in assessing severity of DR. However, the use of mydriatic 2F handheld imaging only meets the minimum standards for sensitivity and specificity for refDR but not for vtDR. When using handheld cameras, the addition of peripheral fields in 5F imaging further refines the referral approach by decreasing ungradable rate and increasing sensitivity for vtDR.

1.
Pasquel
FJ
,
Hendrick
AM
,
Ryan
M
,
Cason
E
,
Ali
MK
,
Narayan
KMV
.
Cost-effectiveness of different diabetic retinopathy screening modalities
.
J Diabetes Sci Technol
.
2015
;
10
(
2
):
301
7
.
2.
Salongcay
RP
,
Silva
PS
.
The role of teleophthalmology in the management of diabetic retinopathy
.
Asia-Pacific J Ophthalmol
.
2018
;
7
(
1
):
17
21
.
3.
Avidor
DLA
,
Loewenstein
A
,
Waisbourd
M
,
Nutman
A
.
Cost-effectiveness of diabetic retinopathy screening programs using telemedicine: a systematic review
.
Cost Eff Resour Alloc
.
2020
;
18
:
16
.
4.
Goh
JKH
,
Cheung
CY
,
Sim
SS
,
Tan
PC
,
Tan
GSW
,
Wong
TY
.
Retinal imaging techniques for diabetic retinopathy screening
.
J Diabetes Sci Technol
.
2016
;
10
(
2
):
282
94
.
5.
Tayapad
JB
,
Bengzon
AU
,
Valero
SO
,
Arroyo
MH
,
Papa
RTM
,
Fortuna
EJS
.
Implementation and pilot data on diabetic retinopathy in a teleophthalmology program at a multispecialty primary care clinic philipp
.
J Ophthalmol
.
2014
;
39
(
2
):
90
3
.
6.
Moss
SE
,
Meuer
SM
,
Klein
R
,
Hubbard
LD
,
Brothers
RJ
,
Klein
BE
.
Are seven standard photographic fields necessary for classification of diabetic retinopathy
.
Invest Ophthalmol Vis Sci
.
1989 May
30
5
823
8
.
7.
Møller
F
,
Hansen
M
,
Sjølie
AK
.
Is one 60° fundus photograph sufficient for screening of proliferative diabetic retinopathy
.
Diabetes Care
.
2001
;
24
(
12
):
2083
5
.
8.
Perrier
M
,
Boucher
MC
,
Angioi
K
,
Gresset
JA
,
Olivier
S
.
Comparison of two, three and four 45 degrees image fields obtained with the Topcon CRW6 nonmydriatic camera for screening for diabetic retinopathy
.
Can J Ophthalmol
.
2003
;
38
(
7
):
569
74
.
9.
Aptel
F
,
Denis
P
,
Rouberol
F
,
Thivolet
C
.
Screening of diabetic retinopathy: effect of field number and mydriasis on sensitivity and specificity of digital fundus photography
.
Diabetes Metab
.
2008
;
34
(
3
):
290
3
.
10.
Nsiah-Kumi
P
,
Ortmeier
SR
,
Brown
AE
.
Disparities in diabetic retinopathy screening and disease for racial and ethnic minority populations: a literature review
.
J Natl Med Assoc
.
2009
;
101
(
5
):
430
7
.
11.
Salongcay
RP
,
Aquino
LAC
,
Salva
CMG
,
Saunar
AV
,
Alog
GP
,
Sun
JK
.
Comparison of handheld retinal imaging with ETDRS 7-standard field photography for diabetic retinopathy and diabetic macular edema
.
Ophthalmol Retina
.
2022 Jul
6
7
548
56
.
12.
Wu
L
,
Fernandez-Loaiza
P
,
Sauma
J
,
Hernandez-Bogantes
E
,
Masis
M
.
Classification of diabetic retinopathy and diabetic macular edema
.
World J Diabetes
.
2013
;
4
(
6
):
290
4
.
13.
Scanlon
PH
.
Update on screening for sight-threatening diabetic retinopathy
.
Ophthalmic Res
.
2019
;
62
(
4
):
218
24
.
14.
Silva
PS
,
Aiello
LP
.
Telemedicine and eye examinations for diabetic retinopathy
.
JAMA Ophthalmol
.
2015
;
133
(
5
):
525
6
.
15.
Fathy
CPS
,
Patel
S
,
Sternberg
P
Jr
,
Kohanim
S
.
Disparities in adherence to screening guidelines for diabetic retinopathy in the United States: a comprehensive review and guide for future directions
.
Semin Ophthalmol
.
2016
;
31
(
4
):
364
77
.
16.
Bresnick
G
,
Cuadros
JA
,
Khan
M
,
Fleischmann
S
,
Wolff
G
,
Limon
A
.
Adherence to ophthalmology referral, treatment and follow-up after diabetic retinopathy screening in the primary care setting
.
BMJ Open Diabetes Res Care
.
2020
;
8
(
1
):
e001154
.
17.
Srihatrai
PHT
,
Hlowchitsieng
T
.
The diagnostic accuracy of single- and five-field fundus photography in diabetic retinopathy screening by primary care physicians
.
Indian J Ophthalmol
.
2018
;
66
(
1
):
94
7
.
18.
Jacoba
CMP
,
Salva
CM
,
Salongcay
RP
,
Sun
JK
,
Peto
T
.
Comparisons of handheld retinal imaging devices with ultrawide field (UWF) and early treatment diabetic retinopathy study (ETDRS) photographs for determining diabetic retinopathy (DR) severity
.
IOVS
.
2022
63
7
).
19.
Vujosevic
S
,
Benetti
E
,
Massignan
F
,
Pilotto
E
,
Varano
M
,
Cavarzeran
F
.
Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields
.
Am J Ophthalmol
.
2009
;
148
(
1
):
111
8
.
20.
Grzybowski
ABP
,
Brona
P
,
Lim
G
,
Ruamviboonsuk
P
,
Tan
GSW
,
Abramoff
M
.
Artificial intelligence for diabetic retinopathy screening: a review
.
Eye
.
2020
;
34
(
3
):
451
60
.
21.
Doan
D
,
Aquino
LA
,
Silva
JPY
,
Salva
CM
,
Jacoba
CMP
,
Salongcay
R
.
Automated machine learning (AutoML) models for diabetic retinopathy (DR) image classification from handheld retinal images
.
Invest Ophthalmol Vis Sci
.
2022
;
63
(
7
):
2105
F0094
.
You do not currently have access to this content.