Purpose: The aim of this article was to describe recent advances in the use of new technology in diabetic retinopathy screening by looking at studies that assessed the effectiveness and cost-effectiveness of these technologies. Methods: The author conducts an ongoing search for articles relating to screening or management of diabetic retinopathy utilising Zetoc with keywords and contents page lists from relevant journals. Results: The areas discussed in this article are reference standards, alternatives to digital photography, area of retina covered by the screening method, size of the device and hand-held cameras, mydriasis versus non-mydriasis or a combination, measurement of distance visual acuity, grading of images, use of automated grading analysis and cost-effectiveness of the new technologies. Conclusions: There have been many recent advances in technology that may be adopted in the future by screening programmes for sight-threatening diabetic retinopathy but each device will need to demonstrate effectiveness and cost-effectiveness before more widespread adoption.

The Wilson and Junger criteria, which are the 1968 principles [1] applied by the World Health Organisation, have formed the basis of development of screening programmes and required an evidence base which I adapted [2] for sight-threatening diabetic retinopathy (STDR):

1.STDR is an important public health problem [3, 4]

2.The incidence of STDR is going to remain the same or become an even greater public health problem [5, 6]

3.STDR has a recognisable latent or early symptomatic stage [7-9]

4.Treatment for STDR is effective and agreed upon universally

Diabetic retinopathy (DR) can be prevented or the rate of deterioration reduced by improved control of blood glucose [10-12] and blood pressure [13, 14]. Laser treatment is effective [15, 16], and vascular endothelial growth factor inhibitors can improve the results of treatment in diabetic maculopathy [17, 18] and in some cases of proliferative DR [19, 20].

In this article I have concentrated on reviewing the updates in relation to the final two criteria:

5.The test – a suitable and reliable screening test is available, acceptable to both health care professionals and (more importantly) to the public

6.Cost-effectiveness – the costs of screening and effective treatment of STDR are balanced economically in relation to total expenditure on health care – including the consequences of leaving the disease untreated

The review of the literature relating to screening for DR has been ongoing since March 2000. The methodology involves a search technique for articles relating to screening or management of DR utilising Zetoc (http://zetoc.jisc.ac.uk/), which is a comprehensive research database, giving you access to over 34,500 journals and more than 55 million article citations and conference papers through the British Library’s electronic table of contents covering 1993 to the present day and is updated daily.

Subject title keywords are searched daily using 21 different combinations (e.g., “retinopathy” or “digital” and “imaging” and “eye” in title), and contents page lists from 28 journals are reviewed monthly. Articles of interest identified with this search strategy were sourced from online electronic journal resources (e.g., Open Athens [21] or the Royal Society of Medicine [22]).

The Test

Reference Standards for Digital Photographic and Other Screening Methods

There are two accepted reference standards to compare with any new screening methodology.

(a) 7-field (30-degree) stereo photography is considered the best reference standard.

The advantage of this reference standard is the area of retina covered and the detailed grading classification [23] which has been developed for this standard. The disadvantage is that the unassessable image rate is at 10% in one report from the Wisconsin Epidemiological Study of Diabetic Retinopathy [24] and, in many studies, not reported so is likely higher than that rate.

(b) Slit lamp biomicroscopy by an ophthalmologist is another accepted reference standard, although it is preferable with this methodology to use one or a small number of retinal specialists. The studies demonstrate significant variation compared to 7-field stereophotography with some studies in which the ophthalmologists performed poorly [25, 26], and others with better results [27, 28]. Gangaputra et al. [29] compared evaluation by clinical examination with image grading at a reading centre for the classification of DR and diabetic macular oedema and concluded that the results support the use of clinical information for defining broad severity categories but not for documenting small-to-moderate changes in DR over time.

Gangaputra et al. [30] also compared 35-mm film with digital photography and found that agreement between film and digital images was substantial to almost perfect for DR severity level and moderate to substantial for diabetic macular oedema and clinically significant macular oedema severity levels, respectively. The study concluded that replacement of film fundus images with digital images for DR severity level should not adversely affect clinical trial quality.

The “Exeter Standards,” which were a consensus view formed at a meeting [31] in Exeter in the UK in 1988, formed the basis for a publication [32] for an acceptable method for use in a systematic screening programme for DR in the UK, which was adopted in the planning [33] of the English NHS Diabetic Eye Screening Programme. The Exeter Standards recommended that a screening test for STDR should achieve a minimum sensitivity of 80% and a minimum specificity of 95%.

A systematic review by Piyasena et al. [34] found that both mydriatic and non-mydriatic digital imaging methods generate a satisfactory level of sensitivity. The mean proportion of ungradable images in non-mydriatic methods was 18.4% (CI 13.6–23.3%) and for the mydriatic method 6.2% (CI 1.7–10.8%) and, once these were excluded from analysis:

(a) the 1-field non-mydriatic strategy gave summary estimates of sensitivity of 78% (CI 76–80%) and of specificity of 91% (CI 90–92%); the 2-field non-mydriatic strategy gave summary estimates of sensitivity of 91% (95% CI 90–93%) and of specificity of 94% (CI 93–95%);

(b) the 1-field mydriatic strategy gave summary estimates of sensitivity of 80% (CI 77–82%) and of specificity of 93% (CI 92–94%); the 2-field mydriatic strategy gave summary estimates of sensitivity of 85% (95% CI 84–87%) and of specificity of 82% (95% CI 81–83%).

The article concluded that, overall, there was no difference in sensitivity between non-mydriatic and mydriatic methods (86%, 95% CI 85–87%) after exclusion of ungradable images.

In the literature, studies vary as to whether they count ungradable images as test positive, and it is more likely that a study will achieve the 95% specificity if they do not count ungradable images as test positive.

Alternatives to Digital Photography

Goh et al. [35] produced a comprehensive review of retinal imaging techniques for DR screening. The most exciting new technologies that may be used in screening in the future, providing they can be shown to be effective and cost-effective, are the scanning confocal ophthalmoscopes that use either laser light or light-emitting diodes (LED). Examples of 4 CE-marked scanning confocal ophthalmoscopes that are currently commercially available are discussed:

The Optos California which is described as ultrawide-field imaging incorporates low-powered laser wavelengths in red (635 nm), green (532 nm) and blue (488 nm) that scan simultaneously and produce a composite image that joins the 3 wavelengths of light into a false-colour image. In 2016, Silva et al. [36] compared the efficiency of non-mydriatic ultrawide-field imaging and non-mydriatic fundus photography in a DR ocular telehealth programme.

The Heidelberg Spectralis OCT2 with multicolour functionality also uses three laser wavelengths, blue (488 nm), green (515 nm) and infrared reflectance (820 nm), to simultaneously capture a composite false-colour image.

The Eidon confocal scanner (Centervue, Padova, Italy) combines confocal imaging with natural white-light illumination to provide a true-colour image using a white LED (440–650 nm).

The Zeiss Clarus 500 uses red (585–640 nm), green (500–585 nm) and blue (435–500 nm) LEDs to capture a composite image.

There have not yet been any major studies published using any of these imaging techniques in a DR screening setting.

The Area of Retina Covered by the Screening Method

The original 35-mm film fundus cameras that were used for 7-field stereophotography had 30-degree fields. In 1989, Moss et al. [24] demonstrated that for 8 retinopathy levels, the rate of agreement with 7 stereoscopic fields ranges from 80% for two 30-degree stereo fields to 91% for four 30-degree stereo fields.

The non-mydriatic digital fundus cameras that are widely used in screening programmes, whether or not the patient’s eyes are dilated, usually have 45-degree fields. Population-based screening programmes that utilise non-mydriatic photography commonly capture a single 45-degree field centred on the fovea of each eye [37]. For many mydriatic schemes, two 45-degree fields are taken [38] – one centred on the fovea and one on the optic disc.

The Scanning Confocal Ophthalmoscopes have the fields of view shown below:

(a) Heidelberg Spectralis OCT2 with multicolour functionality: 1-field or 2-field non-mydriatic 55-degree image(s) per eye (when using supplementary lens)

(b) Optos California: 1-field non-mydriatic 200-degree image per eye

(c) Zeiss Clarus 500: 1-field non-mydriatic 130-degree image per eye

(d) CentreVue Eidon: 1- or 2-field non-mydriatic 60-degree image(s) per eye

Size of the Device and Hand-Held Cameras

There have been many claims for the use of smartphones in DR screening. There is an excellent review of potential devices by Bolster et al. [39]. Hand-held devices have historically performed poorly in DR screening [40] although a recent study suggested that they could be used for optic disc imaging [41] and another study suggested that a small device had been validated [42] for DR screening. The latter was an excellent study that compared the sensitivity and specificity of a “fundus on phone” camera, a smartphone-based retinal imaging system, as a screening tool for DR detection and DR severity in comparison with 7-standard field digital retinal photography. It was noteworthy that mydriasis was used and that the smartphone was fixed and the patient’s head positioned using a slit lamp chin rest, overcoming many of the problems of movement of patient and operator that is associated with hand-held devices. It may be that the way forward with these small devices is to use an inexpensive device to fix them and a slit lamp chin rest for the patient.

Mydriasis versus Non-Mydriasis or a Combination of Both

A strong correlation has been reported [43] between older age and poor-quality image rate in non-mydriatic digital photography in DR screening. The main reason for this is higher rates of media opacity and smaller pupil sizes in older people. Scanlon et al. [44] reported an ungradable image rate for non-mydriatic photography of 19.7% (95% CI 18.4–21.0%), and Murgatroyd et al. [45] reported an ungradable image rate for non-mydriatic photography of 26%. The mean age of the patients in the study of Scanlon et al. [44] was 65 years, and in that of Murgatroyd et al. [45] the median age of the patients was 63.0 years (range 17–88 years, interquartile range 51.8–70.3 years). There is also an ethnicity component with some studies demonstrating poorer results for non-mydriatic digital photographic screening in eyes with more iris pigmentation [46]. Scotland introduced the concept of staged mydriasis into their screening programme, only dilating those who the technician taking the images determined had poor-quality images without mydriasis. As the age of the Scottish population has increased, the numbers needing dilation have risen to 34% [pers. commun. Mike Black, Scottish DRS Collaborative Coordinator].

Silva et al. [47] have demonstrated that the ungradable rate per patient for DR and diabetic macular oedema was significantly lower with non-mydriatic ultrawide-field imaging compared with non-mydriatic fundus photography (DR, 2.8 vs. 26.9%, p < 0.0001; diabetic macular oedema, 3.8 vs. 26.2%, p < 0.0001) in the Indian Health Service-JVN programme, which serves American Indian and Alaska Native communities.

Measurement of Distance Visual Acuity

Visual acuity is widely accepted as an adjunct to screening for diabetic maculopathy, but in isolation it is not sufficiently sensitive to be a screening tool [48, 49], and there is currently no study that supports the added benefit of visual acuity in screening. It is however, from the patient’s perspective, probably the most important factor.

Grading the Images

In most screening programmes, trained graders grade the images, and the ones with the severer pathology are referred to ophthalmologists to decide on further management. Different grading criteria are used in different countries.

Use of Automated Analysis for Grading

Automated grading of images from DR screening has been pioneered in Scotland with the development of iGradingM (Scottish Health Innovations Ltd.) which has been used extensively as first level disease/no disease grader [50]. This includes an image quality assessment to reduce the workload of manual grading in the Scottish screening programme which takes 1-field non-mydriatic photographs.

Tufail et al. [51] reported on a study which included a total of 20,258 patients with 102,856 two-field per eye images. Three software products were tested, iGradingM (Scottish Health Innovations Ltd.), EyeArt (Eyenuk Inc., Woodland Hills, CA, USA) and Retmarker (Retmarker Ltd., Coimbra, Portugal), with the following sensitivities: EyeArt 94.7% (95% CI 94.2–95.2%) for any retinopathy (manual grades R1, U, M1, R2 and R3 as refined by arbitration), 93.8% (95% CI 92.9–94.6%) for referable retinopathy; corresponding sensitivities for Retmarker were 73.0% (95% CI 72.0–74.0%) for any retinopathy, 85.0% (95% CI 83.6–86.2%) for referable retinopathy. For manual grades R0 and no maculopathy (M0), specificity was 20% (95% CI 19–21%) for EyeArt and 53% (95% CI 52–54%) for Retmarker. In this study the version of iGradingM was unable to grade the nasal field.

The Iowa Detection Program (IDx-DR) is another software solution for automated grading that was tested [52] in the Hoorn Diabetes Care System in the Netherlands.

There are also a number of developing systems [53, 54] that are not yet commercially available.

Automated analysis of OCT images through use of deep learning is being explored in a collaborative project between Moorfields Eye Hospital and Google DeepMind [55] and in the Singapore Eye Research Centre [35].

A recent study [56] examined the variability in different methods of grading, definitions of reference standards, and their effects on building deep learning models for the detection of diabetic eye disease. The results from the studies are very dependent on the image sets that they are being tested upon.

Cost-Effectiveness

The cost-effectiveness of screening for STDR is dependent on the local health care system but there are various reports of screening being cost-effective in health care settings such as Singapore [57], Canada [58], South Africa [59] and India [60] with the proviso that low-risk groups can be identified and cost-effectiveness of screening for STDR can be improved in some settings by differential or individualised screening intervals for low- and high-risk groups [61-63]. Automated grading was shown to be cost-effective in the Scottish Screening Programme [64, 65], and the Tufail study [51] reported that two of the software packages that they tested (Retmarker and EyeArt) achieved acceptable sensitivity for referable retinopathy and false-positive rates (compared with human graders as reference standard) and appear to be cost-effective.

The use of OCT in screening has been considered but the cost of the equipment makes it more likely that this would only be useful as a second-line screening tool [66, 67] for those who are screen positive with 2-dimensional photographic markers for diabetic maculopathy.

With respect to the use of ultrawide-field imaging systems in DR screening programmes, a review by Fenner et al. [68] summed up the current situation that, despite the impressive outcomes in clinical trials, it remains unclear whether the cost savings of reduced inappropriate referrals are sufficient to justify the financial outlay.

There have been many recent advances in technology that may be adopted by screening programmes for STDR in the future.

Most screening programmes currently use staged mydriasis with one 45-degree field non-mydriatic digital photography or two 45-degree field mydriatic digital photography. Advances in camera technology and in particular scanning confocal ophthalmoscopes with laser light or light-emitting diodes show good potential for non-mydriatic photography with wider fields. Each device will need to demonstrate effectiveness and cost-effectiveness before more widespread adoption. Automated reading of images is progressing, with Scotland having already introduced this into their national programmes and other countries likely to follow in the future.

The author has no ethical conflicts to disclose.

I have attended Advisory Boards for Pfizer, Allergan, Roche, Bayer and Boehringer.

My department has received Educational, Research and Audit Grants from Allergan, Pfizer, Novartis, Boehringer and Bayer in the last 10 years.

No funding has been received for the preparation of this manuscript.

1.
Wilson
J
,
Jungner
G
.
The principles and practice of screening for disease. Public Health Papers 34. Public Health Papers
.
Geneva
:
WHO
;
1968
.
2.
Scanlon
P
.
An evaluation of the effectiveness and cost-effectiveness of screening for diabetic retinopathy by digital imaging photography and technician ophthalmoscopy and the subsequent change in activity, workload and costs of new diabetic ophthalmology referrals
.
London
:
UCL
;
2005
.
3.
Zheng
Y
,
He
M
,
Congdon
N
.
The worldwide epidemic of diabetic retinopathy
.
Indian J Ophthalmol
.
2012
Sep-Oct
;
60
(
5
):
428
31
.
[PubMed]
0301-4738
4.
Yau
JW
,
Rogers
SL
,
Kawasaki
R
,
Lamoureux
EL
,
Kowalski
JW
,
Bek
T
, et al;
Meta-Analysis for Eye Disease (META-EYE) Study Group
.
Global prevalence and major risk factors of diabetic retinopathy
.
Diabetes Care
.
2012
Mar
;
35
(
3
):
556
64
.
[PubMed]
0149-5992
5.
IDF
.
International Diabetes Federation.
IDF Diabetes Atlas Brussels, Belgium
2013
[16/04/2014]. 6th edition:[Available from: http://www.idf.org/diabetesatlas
6.
Stefánsson
E
.
Prevention of diabetic blindness
.
Br J Ophthalmol
.
2006
Jan
;
90
(
1
):
2
3
.
[PubMed]
0007-1161
7.
Early Treatment Diabetic Retinopathy Study Research Group
.
Fundus photographic risk factors for progression of diabetic retinopathy. ETDRS report number 12
.
Ophthalmology
.
1991
May
;
98
(
5
Suppl
):
823
33
.
[PubMed]
0161-6420
8.
Stratton
IM
,
Aldington
SJ
,
Taylor
DJ
,
Adler
AI
,
Scanlon
PH
.
A simple risk stratification for time to development of sight-threatening diabetic retinopathy
.
Diabetes Care
.
2013
Mar
;
36
(
3
):
580
5
.
[PubMed]
0149-5992
9.
Ruta
LM
,
Magliano
DJ
,
Lemesurier
R
,
Taylor
HR
,
Zimmet
PZ
,
Shaw
JE
.
Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries
.
Diabet Med
.
2013
Apr
;
30
(
4
):
387
98
.
[PubMed]
0742-3071
10.
Nathan
DM
,
Genuth
S
,
Lachin
J
,
Cleary
P
,
Crofford
O
,
Davis
M
, et al;
Diabetes Control and Complications Trial Research Group
.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus
.
N Engl J Med
.
1993
Sep
;
329
(
14
):
977
86
.
[PubMed]
0028-4793
11.
Stratton
IM
,
Kohner
EM
,
Aldington
SJ
,
Turner
RC
,
Holman
RR
,
Manley
SE
, et al
UKPDS 50: risk factors for incidence and progression of retinopathy in Type II diabetes over 6 years from diagnosis
.
Diabetologia
.
2001
Feb
;
44
(
2
):
156
63
.
[PubMed]
0012-186X
12.
Ferris
FL
 3rd
,
Nathan
DM
.
Preventing Diabetic Retinopathy Progression
.
Ophthalmology
.
2016
Sep
;
123
(
9
):
1840
2
.
[PubMed]
0161-6420
13.
Matthews
DR
,
Stratton
IM
,
Aldington
SJ
,
Holman
RR
,
Kohner
EM
;
UK Prospective Diabetes Study Group
.
Risks of progression of retinopathy and vision loss related to tight blood pressure control in type 2 diabetes mellitus: UKPDS 69
.
Arch Ophthalmol
.
2004
Nov
;
122
(
11
):
1631
40
.
[PubMed]
0003-9950
14.
Chase
HP
,
Garg
SK
,
Jackson
WE
,
Thomas
MA
,
Harris
S
,
Marshall
G
, et al
Blood pressure and retinopathy in type I diabetes
.
Ophthalmology
.
1990
Feb
;
97
(
2
):
155
9
.
[PubMed]
0161-6420
15.
Davies
EG
,
Petty
RG
,
Kohner
EM
.
Long term effectiveness of photocoagulation for diabetic maculopathy
.
Eye (Lond)
.
1989
;
3
(
Pt 6
):
764
7
.
[PubMed]
0950-222X
16.
Chew
EY
,
Ferris
FL
 3rd
,
Csaky
KG
,
Murphy
RP
,
Agrón
E
,
Thompson
DJ
, et al
The long-term effects of laser photocoagulation treatment in patients with diabetic retinopathy: the early treatment diabetic retinopathy follow-up study
.
Ophthalmology
.
2003
Sep
;
110
(
9
):
1683
9
.
[PubMed]
0161-6420
17.
Elman
MJ
,
Qin
H
,
Aiello
LP
,
Beck
RW
,
Bressler
NM
,
Ferris
FL
 3rd
, et al;
Diabetic Retinopathy Clinical Research Network
.
Intravitreal ranibizumab for diabetic macular edema with prompt versus deferred laser treatment: three-year randomized trial results
.
Ophthalmology
.
2012
Nov
;
119
(
11
):
2312
8
.
[PubMed]
0161-6420
18.
Wells
JA
,
Glassman
AR
,
Ayala
AR
,
Jampol
LM
,
Bressler
NM
,
Bressler
SB
, et al;
Diabetic Retinopathy Clinical Research Network
.
Aflibercept, Bevacizumab, or Ranibizumab for Diabetic Macular Edema: Two-Year Results from a Comparative Effectiveness Randomized Clinical Trial
.
Ophthalmology
.
2016
Jun
;
123
(
6
):
1351
9
.
[PubMed]
0161-6420
19.
Sivaprasad
S
,
Prevost
AT
,
Vasconcelos
JC
,
Riddell
A
,
Murphy
C
,
Kelly
J
, et al;
CLARITY Study Group
.
Clinical efficacy of intravitreal aflibercept versus panretinal photocoagulation for best corrected visual acuity in patients with proliferative diabetic retinopathy at 52 weeks (CLARITY): a multicentre, single-blinded, randomised, controlled, phase 2b, non-inferiority trial
.
Lancet
.
2017
Jun
;
389
(
10085
):
2193
203
.
[PubMed]
0140-6736
20.
Gross
JG
,
Glassman
AR
,
Liu
D
,
Sun
JK
,
Antoszyk
AN
,
Baker
CW
, et al;
Diabetic Retinopathy Clinical Research Network
.
Five-Year Outcomes of Panretinal Photocoagulation vs Intravitreous Ranibizumab for Proliferative Diabetic Retinopathy: A Randomized Clinical Trial
.
JAMA Ophthalmol
.
2018
Oct
;
136
(
10
):
1138
48
.
[PubMed]
2168-6165
21.
Open Athens
.
2019
[22/02/2019]. Available from: https://www.openathens.net/
22.
Royal Society of Medicine
.
2019
[22/02/2019]. Available from: https://www.rsm.ac.uk/the-library/
23.
Early Treatment Diabetic Retinopathy Study Research Group
.
Grading diabetic retinopathy from stereoscopic color fundus photographs—an extension of the modified Airlie House classification. ETDRS report number 10
.
Ophthalmology
.
1991
May
;
98
(
5
Suppl
):
786
806
.
[PubMed]
0161-6420
24.
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
.
[PubMed]
0146-0404
25.
Lin
DY
,
Blumenkranz
MS
,
Brothers
RJ
,
Grosvenor
DM
.
The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography
.
Am J Ophthalmol
.
2002
Aug
;
134
(
2
):
204
13
.
[PubMed]
0002-9394
26.
Pugh
JA
,
Jacobson
JM
,
Van Heuven
WA
,
Watters
JA
,
Tuley
MR
,
Lairson
DR
, et al
Screening for diabetic retinopathy. The wide-angle retinal camera
.
Diabetes Care
.
1993
Jun
;
16
(
6
):
889
95
.
[PubMed]
0149-5992
27.
Kinyoun
J
,
Barton
F
,
Fisher
M
,
Hubbard
L
,
Aiello
L
,
Ferris
F
 3rd
;
The ETDRS Research Group
.
Detection of diabetic macular edema. Ophthalmoscopy versus photography—Early Treatment Diabetic Retinopathy Study Report Number 5
.
Ophthalmology
.
1989
Jun
;
96
(
6
):
746
50
.
[PubMed]
0161-6420
28.
Scanlon
PH
,
Malhotra
R
,
Greenwood
RH
,
Aldington
SJ
,
Foy
C
,
Flatman
M
, et al
Comparison of two reference standards in validating two field mydriatic digital photography as a method of screening for diabetic retinopathy
.
Br J Ophthalmol
.
2003
Oct
;
87
(
10
):
1258
63
.
[PubMed]
0007-1161
29.
Gangaputra
S
,
Lovato
JF
,
Hubbard
L
,
Davis
MD
,
Esser
BA
,
Ambrosius
WT
, et al;
ACCORD Eye Research Group
.
Comparison of standardized clinical classification with fundus photograph grading for the assessment of diabetic retinopathy and diabetic macular edema severity
.
Retina
.
2013
Jul-Aug
;
33
(
7
):
1393
9
.
[PubMed]
0275-004X
30.
Gangaputra
S
,
Almukhtar
T
,
Glassman
AR
,
Aiello
LP
,
Bressler
N
,
Bressler
SB
, et al;
Diabetic Retinopathy Clinical Research Network
.
Comparison of film and digital fundus photographs in eyes of individuals with diabetes mellitus
.
Invest Ophthalmol Vis Sci
.
2011
Aug
;
52
(
9
):
6168
73
.
[PubMed]
0146-0404
31.
Marshall
S
.
The Exeter BDA Meeting - a Synopsis
.
Diabet Med
.
1988
;
5
S1
:
iii
iv
. 0742-3071
32.
BDA
.
Retinal photography screening for diabetic eye disease.
London: British Diabetic Association,
1997
September 1997. Report No.
33.
Garvican
L
,
Clowes
J
,
Gillow
T
.
Preservation of sight in diabetes: developing a national risk reduction programme
.
Diabet Med
.
2000
Sep
;
17
(
9
):
627
34
.
[PubMed]
0742-3071
34.
Piyasena
MM
,
Murthy
GV
,
Yip
JL
,
Gilbert
C
,
Peto
T
,
Gordon
I
, et al
Systematic review and meta-analysis of diagnostic accuracy of detection of any level of diabetic retinopathy using digital retinal imaging
.
Syst Rev
.
2018
Nov
;
7
(
1
):
182
.
[PubMed]
2046-4053
35.
Goh
JK
,
Cheung
CY
,
Sim
SS
,
Tan
PC
,
Tan
GS
,
Wong
TY
.
Retinal Imaging Techniques for Diabetic Retinopathy Screening
.
J Diabetes Sci Technol
.
2016
Feb
;
10
(
2
):
282
94
.
[PubMed]
1932-2968
36.
Silva
PS
,
Cavallerano
JD
,
Haddad
NM
,
Tolls
D
,
Thakore
K
,
Patel
B
, et al
Comparison of nondiabetic retinal findings identified with Nonmydriatic Fundus photography vs Ultrawide field imaging in an ocular Telehealth program
.
JAMA Ophthalmol
.
2016
Mar
;
134
(
3
):
330
4
.
[PubMed]
2168-6165
37.
Williams
GA
,
Scott
IU
,
Haller
JA
,
Maguire
AM
,
Marcus
D
,
McDonald
HR
.
Single-field fundus photography for diabetic retinopathy screening: a report by the American Academy of Ophthalmology
.
Ophthalmology
.
2004
May
;
111
(
5
):
1055
62
.
[PubMed]
0161-6420
38.
Scanlon
PH
.
The English National Screening Programme for diabetic retinopathy 2003-2016
.
Acta Diabetol
.
2017
Jun
;
54
(
6
):
515
25
.
[PubMed]
0940-5429
39.
Bolster
NM
,
Giardini
ME
,
Bastawrous
A
.
The Diabetic Retinopathy Screening Workflow: Potential for Smartphone Imaging
.
J Diabetes Sci Technol
.
2015
Nov
;
10
(
2
):
318
24
.
[PubMed]
1932-2968
40.
Yogesan
K
,
Constable
IJ
,
Barry
CJ
,
Eikelboom
RH
,
McAllister
IL
,
Tay-Kearney
ML
.
Telemedicine screening of diabetic retinopathy using a hand-held fundus camera
.
Telemed J
.
2000
;
6
(
2
):
219
23
.
[PubMed]
1078-3024
41.
Bastawrous
A
,
Giardini
ME
,
Bolster
NM
,
Peto
T
,
Shah
N
,
Livingstone
IA
, et al
Clinical Validation of a Smartphone-Based Adapter for Optic Disc Imaging in Kenya
.
JAMA Ophthalmol
.
2016
Feb
;
134
(
2
):
151
8
.
[PubMed]
2168-6165
42.
Rajalakshmi
R
,
Arulmalar
S
,
Usha
M
,
Prathiba
V
,
Kareemuddin
KS
,
Anjana
RM
, et al
Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening
.
PLoS One
.
2015
Sep
;
10
(
9
):
e0138285
.
[PubMed]
1932-6203
43.
Scanlon
PH
,
Foy
C
,
Malhotra
R
,
Aldington
SJ
.
The influence of age, duration of diabetes, cataract, and pupil size on image quality in digital photographic retinal screening
.
Diabetes Care
.
2005
Oct
;
28
(
10
):
2448
53
.
[PubMed]
0149-5992
44.
Scanlon
PH
,
Malhotra
R
,
Thomas
G
,
Foy
C
,
Kirkpatrick
JN
,
Lewis-Barned
N
, et al
The effectiveness of screening for diabetic retinopathy by digital imaging photography and technician ophthalmoscopy
.
Diabet Med
.
2003
Jun
;
20
(
6
):
467
74
.
[PubMed]
0742-3071
45.
Murgatroyd
H
,
Ellingford
A
,
Cox
A
,
Binnie
M
,
Ellis
JD
,
MacEwen
CJ
, et al
Effect of mydriasis and different field strategies on digital image screening of diabetic eye disease
.
Br J Ophthalmol
.
2004
Jul
;
88
(
7
):
920
4
.
[PubMed]
0007-1161
46.
Gupta
V
,
Bansal
R
,
Gupta
A
,
Bhansali
A
.
Sensitivity and specificity of nonmydriatic digital imaging in screening diabetic retinopathy in Indian eyes
.
Indian J Ophthalmol
.
2014
Aug
;
62
(
8
):
851
6
.
[PubMed]
0301-4738
47.
Silva
PS
,
Horton
MB
,
Clary
D
,
Lewis
DG
,
Sun
JK
,
Cavallerano
JD
, et al
Identification of Diabetic Retinopathy and Ungradable Image Rate with Ultrawide Field Imaging in a National Teleophthalmology Program
.
Ophthalmology
.
2016
Jun
;
123
(
6
):
1360
7
.
[PubMed]
0161-6420
48.
Scanlon
PH
,
Foy
C
,
Chen
FK
.
Visual acuity measurement and ocular co-morbidity in diabetic retinopathy screening
.
Br J Ophthalmol
.
2008
Jun
;
92
(
6
):
775
8
.
[PubMed]
0007-1161
49.
Corcoran
JS
,
Moore
K
,
Agarawal
OP
,
Edgar
DF
,
Yudkin
J
.
Visual acuity screening for diabetic maculopathy
.
Pract Diabetes
.
1985
;
2
:
230
2
.2047-2897
50.
Fleming
AD
,
Philip
S
,
Goatman
KA
,
Prescott
GJ
,
Sharp
PF
,
Olson
JA
.
The evidence for automated grading in diabetic retinopathy screening
.
Curr Diabetes Rev
.
2011
Jul
;
7
(
4
):
246
52
.
[PubMed]
1573-3998
51.
Tufail
A
,
Kapetanakis
VV
,
Salas-Vega
S
,
Egan
C
,
Rudisill
C
,
Owen
CG
, et al
An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness
.
2016
. 1-73]. Available from: https://www.journalslibrary.nihr.ac.uk/hta/hta20920/#/full-report
52.
van der Heijden
AA
,
Abramoff
MD
,
Verbraak
F
,
van Hecke
MV
,
Liem
A
,
Nijpels
G
.
Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System
.
Acta Ophthalmol
.
2018
Feb
;
96
(
1
):
63
8
.
[PubMed]
1755-375X
53.
Li
Z
,
Keel
S
,
Liu
C
,
He
Y
,
Meng
W
,
Scheetz
J
, et al
An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs
.
Diabetes Care
.
2018
Dec
;
41
(
12
):
2509
16
.
[PubMed]
0149-5992
54.
Ramachandran
N
,
Hong
SC
,
Sime
MJ
,
Wilson
GA
.
Diabetic retinopathy screening using deep neural network
.
Clin Exp Ophthalmol
.
2018
May
;
46
(
4
):
412
6
.
[PubMed]
1442-6404
55.
De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018 Sep;24(9):1342–50.
56.
Krause
J
,
Gulshan
V
,
Rahimy
E
,
Karth
P
,
Widner
K
,
Corrado
GS
, et al
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy
.
Ophthalmology
.
2018
Aug
;
125
(
8
):
1264
72
.
[PubMed]
0161-6420
57.
Nguyen
HV
,
Tan
GS
,
Tapp
RJ
,
Mital
S
,
Ting
DS
,
Wong
HT
, et al
Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore.
OPHTHALMOLOGY -ROCHESTER AND HAGERSTOWN.
2016
;123(12).
58.
Kanjee
R
,
Dookeran
RI
,
Mathen
MK
,
Stockl
FA
,
Leicht
R
.
Six-year prevalence and incidence of diabetic retinopathy and cost-effectiveness of tele-ophthalmology in Manitoba.
Canadian Journal of Ophthalmology / Journal Canadien d'Ophtalmologie.
2016
;51(6):467-70.
59.
Khan
T
,
Bertram
MY
,
Jina
R
,
Mash
B
,
Levitt
N
,
Hofman
K
.
Preventing diabetes blindness: cost effectiveness of a screening programme using digital non-mydriatic fundus photography for diabetic retinopathy in a primary health care setting in South Africa
.
Diabetes Res Clin Pract
.
2013
Aug
;
101
(
2
):
170
6
.
[PubMed]
0168-8227
60.
Rachapelle
S
,
Legood
R
,
Alavi
Y
,
Lindfield
R
,
Sharma
T
,
Kuper
H
, et al
The cost-utility of telemedicine to screen for diabetic retinopathy in India
.
Ophthalmology
.
2013
Mar
;
120
(
3
):
566
73
.
[PubMed]
0161-6420
61.
Scanlon
PH
,
Aldington
SJ
,
Leal
J
,
Luengo-Fernandez
R
,
Oke
J
,
Sivaprasad
S
, et al
Development of a cost-effectiveness model for optimisation of the screening interval in diabetic retinopathy screening
.
Health Technol Assess
.
2015
Sep
;
19
(
74
):
1
116
.
[PubMed]
1366-5278
62.
Scanlon
PH
.
Screening Intervals for Diabetic Retinopathy and Implications for Care
.
Curr Diab Rep
.
2017
Sep
;
17
(
10
):
96
.
[PubMed]
1534-4827
63.
Lund
SH
,
Aspelund
T
,
Kirby
P
,
Russell
G
,
Einarsson
S
,
Palsson
O
, et al
Individualised risk assessment for diabetic retinopathy and optimisation of screening intervals: a scientific approach to reducing healthcare costs
.
Br J Ophthalmol
.
2016
May
;
100
(
5
):
683
7
.
[PubMed]
0007-1161
64.
Scotland
GS
,
McNamee
P
,
Philip
S
,
Fleming
AD
,
Goatman
KA
,
Prescott
GJ
, et al
Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland
.
Br J Ophthalmol
.
2007
Nov
;
91
(
11
):
1518
23
.
[PubMed]
0007-1161
65.
Scotland
GS
,
McNamee
P
,
Fleming
AD
,
Goatman
KA
,
Philip
S
,
Prescott
GJ
, et al;
Scottish Diabetic Retinopathy Clinical Research Network
.
Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy
.
Br J Ophthalmol
.
2010
Jun
;
94
(
6
):
712
9
.
[PubMed]
0007-1161
66.
Prescott
G
,
Sharp
P
,
Goatman
K
,
Scotland
G
,
Fleming
A
,
Philip
S
, et al
Improving the cost-effectiveness of photographic screening for diabetic macular oedema: a prospective, multi-centre, UK study
.
Br J Ophthalmol
.
2014
Aug
;
98
(
8
):
1042
9
.
[PubMed]
0007-1161
67.
Leal
J
,
Luengo-Fernandez
R
,
Stratton
IM
,
Dale
A
,
Ivanova
K
,
Scanlon
PH
.
Cost-effectiveness of digital surveillance clinics with optical coherence tomography versus hospital eye service follow-up for patients with screen-positive maculopathy.
Eye (Lond).
2018
.
68.
Fenner
BJ
,
Wong
RL
,
Lam
WC
,
Tan
GS
,
Cheung
GC
.
Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review
.
Ophthalmol Ther
.
2018
Dec
;
7
(
2
):
333
46
.
[PubMed]
2193-8245
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