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Automated Retinal Image Analysis for Diabetic Retinopathy in Telemedicine


Abstract and Figures

There will be an estimated 552 million persons with diabetes globally by the year 2030. Over half of these individuals will develop diabetic retinopathy, representing a nearly insurmountable burden for providing diabetes eye care. Telemedicine programmes have the capability to distribute quality eye care to virtually any location and address the lack of access to ophthalmic services. In most programmes, there is currently a heavy reliance on specially trained retinal image graders, a resource in short supply worldwide. These factors necessitate an image grading automation process to increase the speed of retinal image evaluation while maintaining accuracy and cost effectiveness. Several automatic retinal image analysis systems designed for use in telemedicine have recently become commercially available. Such systems have the potential to substantially improve the manner by which diabetes eye care is delivered by providing automated real-time evaluation to expedite diagnosis and referral if required. Furthermore, integration with electronic medical records may allow a more accurate prognostication for individual patients and may provide predictive modelling of medical risk factors based on broad population data.
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Automated Retinal Image Analysis for Diabetic
Retinopathy in Telemedicine
Dawn A. Sim &Pearse A. Keane &Adnan Tufail &
Catherine A. Egan &Lloyd Paul Aiello &Paolo S. Silva
#Springer Science+Business Media New York 2015
Abstract There will be an estimated 552 million persons with
diabetes globally by the year 2030. Over half of these individ-
uals will develop diabetic retinopathy, representing a nearly
insurmountable burden for providing diabetes eye care. Tele-
medicine programmes have the capability to distribute quality
eye care to virtually any location and address the lack of ac-
cess to ophthalmic services. In most programmes, there is
currently a heavy reliance on specially trained retinal image
graders, a resource in short supply worldwide. These factors
necessitate an image grading automation process to increase
the speed of retinal image evaluation while maintaining accu-
racy and cost effectiveness. Several automatic retinal image
analysis systems designed for use in telemedicine have recent-
ly become commercially available. Such systems have the
potential to substantially improve the manner by which diabe-
tes eye care is delivered by providing automated real-time
evaluation to expedite diagnosis and referral if required. Fur-
thermore, integration with electronic medical records may
allow a more accurate prognostication for individual patients
and may provide predictive modelling of medical risk factors
based on broad population data.
Keywords Automated retinal image analysis .Te leme dici ne .
Diabetes mellitus .Diabetic retinopathy
ARIA Automated retinal image analysis
JVN Joslin Vision Network
CADe Computer-aided detection
CADx Computer-aided diagnosis
ETDRS Early treatment diabetic retinopathy study
ARIS Automated Retinal Imaging System
PCA Principal component analysis
DRS Diabetic retinopathy study
Eduard Jaeger [1] in 1856 first described the controversial
observations of yellowish spots and extravasationsin the
macula of a diabetic patient. It would take over 113 years
before the Airlie House Symposium on the Treatment of Di-
abetic Retinopathy established basis for the current photo-
graphic method of quantifying the presence and severity of
diabetic retinopathy. The characteristic lesions of diabetic ret-
inopathy may develop in anyone with diabetes mellitus, and
these lesions are presently estimated to affect nearly half of
those diagnosed with diabetes at any given time [25]. The
advances in the medical management of diabetes that begun
with discovery of insulin by Frederick Banting, Charles Best
and colleagues in 1921 [6] have substantially increased patient
survival and life expectancy. Patients with diabetes are now
living longer; however, in doing so, they are at increased risk
This article is part of the Topical Collection on Microvascular
D. A. Sim :L. P. Aiello:P. S . Si l v a ( *)
Department of Ophthalmology, Harvard Medical School and
Beetham Eye Institute, Joslin Diabetes Center, One Joslin Place,
Boston, MA 02215, USA
D. A. Sim :P. A. Keane:A. Tufail :C. A. Egan
NIHR Biomedical Research Centre for Ophthalmology, Moorfields
Eye Hospital NHS Foundation Trust, London, UK
D. A. Sim :P. A. Keane:A. Tufail
Institute of Ophthalmology, University College London,
London, UK
P. S. Silva
Teleophthalmology and Image Reading Center, Philippine Eye
Research Institute, National Institutes of Health, University of the
Philippines, Manila, Philippines
Curr Diab Rep (2015) 15:14
DOI 10.1007/s11892-015-0577-6
for developing diabetes-related microvascular complications,
the most common of which is diabetic retinopathy [4,5].
The current treatments available for diabetic retinopathy are
highly effective in preventing visual loss [710]. Early detec-
tion and accurate evaluation of diabetic retinopathy severity,
coordinated medical care and prompt appropriate treatment
represent an effective paradigm of diabetic eye care. However,
current estimates suggest that access to such care in developed
countries ranges between 60 and 90%, with significantly lower
rates in developing countries [11]. Without access to eye care,
individuals do not benefit from these remarkably effective ther-
apeutic advances achieved over the past century.
Telemedicine programmes addressing diabetic retinopathy
have the potential to distribute quality eye care to virtually
any location and address the lack of access to eye care. In
England and Wales, a systematic population-based diabetic ret-
inopathy screening programme together with documented im-
provements in diabetes medical care may have, for the first time
in at least five decades, resulted in diabetic retinopathy no lon-
ger being the leading cause of blindness among working age
adults [12]. However, with a projected 552 million individuals
with diabetes globally by the year 2030, current telemedicine
programmes will require technical enhancements addressing
image acquisition, automated image analysis algorithms and
predictive biomarkers to successfully manage this epidemic.
This review evaluates the current state-of-the-art in retinal im-
age analysis for diabetic retinopathy telemedicine programmes.
Potential Impact of Automated Retinal Image Analysis
on Diabetic Retinopathy Telemedicine Programmes
With the aid of digital retinal colour photography, telemedi-
cine has allowed for timely and accurate detection of diabetic
retinopathy, especially for populations where eye care deliv-
ery was not previously feasible. Economic analysis of
teleophthalmology programmes for diabetic retinopathy based
on the Joslin Vision Network (JVN) used by three US federal
health care agencies found these telemedicine efforts to be a
less costly and a more effective strategy than conventional
clinic-based ophthalmoscopy for identifying and accurately
determining diabetic retinopathy severity and preventing
cases of severe vision loss [13]. Moreover, the implementation
of the JVN programme in primary care settings over a 5-year
period has resulted in a 50 % increase in diabetic retinopathy
surveillance rate and a proportional 50 % increase in the rate
of laser treatment [14].
Telemedicine programmes for diabetic retinopathy include
the following key clinical components: (1) a remote, reliable,
cost-effective image acquisition system capable of reproduc-
ibly acquiring high quality retinal images; (2) an image read-
ing centre for retinal image analysis to assess diabetic retinop-
athy severity; and (3) a clinical coordinating centre that
communicates the findings to primary care providers and/or
patients and facilitates clinic appointments or therapy as re-
quired. In addition, telemedicine programmes require expen-
sive technical, information technology and administrative
support. Nonetheless, one of the main barriers towards a wider
implementation of teleophthalmology has remained the re-
quirement and costs of trained personnel for image reading
and grading of a large volume of retinal images.
Since its first deployment 10 years ago, the JVN has pro-
vided diabetes care to over 100,000 patients, and the current
database servers contain over 2 million retina images [15].
The Indian Health ServiceJVN Teleophthalmology Program
generated retinal images from 1624 patients in 2003 [14]. This
has now grown to 14,804 patients per year in 2013 with an
average grading time of 10 min per patient. A total of at least
2500 man-hours per year will be required to grade diabetic
retinopathy severity from retinal images within this pro-
gramme alone. In the UK (2010 to 2011), 1.9 million people
were evaluated under the National Health Service Diabetic
Eye Screening Program [16]. With a surveillance rate of over
90 %, the UK programme generates retinal images from at
least 1.7 million patients per a year that have to be graded
manually with an estimated equivalent workload of more than
300,000 man-hours per year. Reading retinal images is a high-
ly skilled process, which requires training, continual quality
control, maintenance of a specialised skill set, and heavy reli-
ance on the experience and knowledge of the individual read-
er. Since trained retinal image readers are expensive and in
limited supply worldwide, it has become a necessity to seek
automation processes in ocular telemedicine in order to in-
crease throughput while maintaining cost effectiveness and
accuracy. Importantly, automation would also benefit the pa-
tient by permitting more rapid diagnosis and the potential for
more time devoted to patient education and possibly real-time
communication with the primary care physician.
For the foreseeable future, human readers will be required
for the purpose of quality control, adjudication, and interpreta-
tion of atypical retinal images. However, rather than fully re-
place the human reader, ARIA could improve workflow, dimin-
ish observer fatigue and reduce bias while performing important
initial triage of low risk images. Ideally, the ARIA system
should link a persons electronic medical record with their ret-
inal images before performing automated grading and diagnosis
at the time of imaging. This will allow relevant individualised
medical information to be considered in the stratification of an
individuals risk profile and facilitate counselling and treatment
or follow-up planning during the imaging visit.
Various ARIA strategies for assessing diabetic retinopathy
severity have been extensively evaluated [1720]. In this re-
view, we examine the potential impact of automated image
analysis on ocular telemedicine for diabetic retinopathy, sum-
marise the different ARIA approaches, and evaluate the suit-
ability of the current ARIA programmes for this indication.
14 Page 2 of 9 Curr Diab Rep (2015) 15:14
Approaches to Automated Retinal Image Analysis
Since the first stereoscopic fundus photograph by Jackson and
Weber in 1886, which required a camera fixed onto the pa-
tients head and a 2.5-min exposure, the documentation of
anatomy and pathological changes in the human retina has
been a subject of interest to ophthalmologists, artists, and en-
gineers alike. However, it was the introduction of high-
resolution digital retinal imaging systems in the 1990s com-
bined with exceptional growth in computing power that per-
mitted the development of computer algorithms capable of
computer-aided detection (CADe), and computer-aided diag-
nosis (CADx). CADe is the identification of pathologic le-
sions. CADx provides a classification that incorporates addi-
tional lesion or clinical information to stratify risk or estimate
the probability of disease. These recent imaging and comput-
ing advances allowed ARIA, a topic of interest for more than
40 years, to finally realistically address clinical applications
over the past decade.
A key component to the telemedicine approach is the clin-
ical validation of retinal image analyses against the current
gold-standard established by the Early Treatment Diabetic
Retinopathy Study (ETDRS) consisting of 30°,stereoscopic,
seven-standard field, colour 35-mm slides [21]. The American
Telemedicine Associated has published position statements in
order to provide standards and guidelines specifically for dia-
betic retinopathy [22,23] and recommends that automated
algorithms be compared to the accuracy and precision provid-
ed by the ETDRS imaging and evaluation standards. Over that
past decade, there has been a host of algorithms developed for
automated detection of diabetic retinopathy, the details of
which has been reviewed elsewhere [17,18,24]. Broadly,
the approach to ARIA can be categorised into two compo-
nents: (1) image quality assessment and (2) image analysis.
Image Quality Assessment
Only recently have digital cameras approached the resolution
attained by their 35-mm film counterparts. Independent com-
parative validation studies [25,26] and analysis from
multicentre clinical trials [27,28]havereportedgoodtoex-
cellent agreement in comparing diabetic retinopathy severity
on film and digital images, indicating that the use of digital
images at their current resolution do not systematically alter
detection of diabetic retinopathy severity. In general, the
agreement between film and digital images is less precise at
two particular points on the severity scale, first in determining
the presence of mild nonproliferative retinopathy (driven pri-
marily by microaneurysms) and at moderate nonproliferative
diabetic retinopathy (driven primarily by intraretinal micro-
vascular abnormalities) [27]. Although modern electronic sen-
sors have low image noise, they can be susceptible to pattern
artefacts due to the grid arrangement of digital sensors. The
addition of Bayer filters is helpful in reducing noise in the
Currently, retinal digital photography has progressed to a
stage where colour retinal photographs can be obtained using
low levels of illumination through an undilated pupil.[29,30].
However, human factors such as movement and positioning
and ocular factors like cataract and reflections from retinal
tissues can produce artefacts. Without pupillary dilation, arte-
facts are observed in 330 % of retinal images to an extent
where they impede human grading [29,31]. Thus, the impor-
tance of good image quality prior to automated image analysis
has been recognised, and much ancillary research has been
conducted in the field of image pre-processing. In general, this
consists at least of comparing the histogram of an image ob-
tained to that of an ideal histogram describing the brightness,
contrast and signal/noise ratio, and/or determination of image
clarity by assessing vessels surrounding the macula [32].
Image acquisition can also be automated, but the rate of
ungradable images needs to be closely evaluated. The Auto-
mated Retinal Imaging System (ARIS
, Visual Pathways,
Inc., Prescott, AZ, USA) [33] and Centervue DRS (Centervue
SpA, Padova, Italy) [34] are imaging systems that may be
programmed to acquire sequential colour digital images of
defined fundus fields using minimally trained technicians.
However, these systems have ungradable rates for individual
images as high as 30 %. The automated image acquisition in
these systems would be helpful in obtaining consistent fields
using minimally trained technician; however, this would need
to be combined with image quality analysis to determine the
need for re-imaging a field. Until much improved automated
acquisition systems are available, image pre-processing will
likely need to be incorporated into automated image acquisi-
tion retinal cameras, allowing real-time assessment of image
quality, providing real-time feedback to the technician and
allowing a patient to be re-imaged at the same visit if the
retinal image does not meet a given standard. Real-time image
quality assessment systems are not yet available commercial-
ly. One such a system is being developed by VisionQuest
Biomedical, LLC, Albuquerque, NM, USA, and has been
validated on 2000 colour retinal images showing 100 % sen-
sitivity and 96 % specificity in identifying rejectedimages
Image Analysis
An important first step of ARIA for diabetic retinopathy be-
gins with identification and localisation of normal anatomy,
better known as segmentation,ofstructuressuchastheoptic
nerve head, fovea and large retinal vessels. The main purposes
of identifying these structures are to exclude them in the anal-
ysis for abnormal lesions and also to determine their locations
for image registration (e.g. alignment with a reference image
or a previously acquired image) and for use as a reference for
Curr Diab Rep (2015) 15:14 Page 3 of 9 14
measuring distances within the image. Localisation of the op-
tic nerve head is a well-established approach. Some examples
of techniques used in this regard include the identification of
high pixel intensity within a retinal image (the optic disc is
usually the brightest spotin a retinal image), principal com-
ponent analysis (PCA; a method used for face recognition
software that differentiates the optic disc from other bright
spots such as exudates or reflections in the retina), the Hough
transform (which utilises shape orientations to plot locations)
and geometrical parametric modelling (a technique that ex-
ploits patterns from which retinal vessels emerge from the
optic disc) [19,24].
Automated detection of abnormal diabetic retinopathy le-
sions was initially performed on fluorescein angiograms [36].
Although fluorescein provides a background with high con-
trast between vascular and nonvascular structures and allows
easier identification of vascular pathology, the requirement of
intravenous infusion precluded its wide-scale application. The
development of ARIA for diabetic retinopathy has therefore
largely focussed on the use of digital colour images of the
retina. The main challenge encountered with processing of
colour images is the presence of numerous distractorswith-
in the retinal image (retinal capillaries, underlying choroidal
vessels, and reflection artefacts),all of which may be confused
with diabetic retinopathy lesions. As a result, much research
has been focussed on the selective identification of diabetic
retinopathy features, including microaneurysms,
heamorrhages, hard or soft exudates, cotton-wool spots and
venous beading. These clinical features have been described
in great detail in the landmark clinical trials, Diabetic Retinop-
athy Study (DRS) and ETDRS. [21,37].
Earlier work in ARIA utilised basic image processing tech-
niques such as thresholding, edge detection, filters and mor-
phological processing [24]. These techniques focussed on
identifying individual diabetic retinopathy features mimicking
what is typically performed with manual human grading of
individual lesions. Techniques on the extraction of
microaneurysms from colour fundus images by the process
of thresholding after excluding normal anatomical structures
have been extensively reported with varying levels of success
[18]. Recent developments in ARIA have moved away from
individual detectors to approaches that include the following:
an ensemble-based approach (integrates components of
microaneurysm detectors) [38], a multiple-lesion approach
(fuses multiple classifiers) [39], intelligent systems (which
require training algorithms with data derived from human op-
erators) [40] and content-based image retrieval techniques
(analyses the contents of large digital image databases and
compares it with an archive of labelled images) [41]. The
details of algorithms and mathematical modelling methods
used and their comparative efficiencies have been published
in several reviews [1719,24]. These emerging trends of
ARIA may prove most relevant to telemedicine given their
potential to go beyond traditional retinal image analysis, pos-
sibly providing more detailed assessment of retinopathy se-
verity and greater accuracy of progression risk. Addition of
electronicmedical record integration may not only allow more
accurate prognostication for individual patients but also pro-
vide predictive modelling of risk factors based on broad pop-
ulation data.
Overview of ARIA Systems Currently Deployed
in Telemedicine and Screening Programmes
Current commercially available systems that have been used
in telemedicine or screening programmes include the
iGradingM (Medalytix Group Ltd), the TRIAD
(Hubble Telemedical, Inc.), Iowa Detection Program (IDx,
LLC), RetmarkerDR (Retmarker Ltd, Coimbra, Portugal)
and Retinalyze System (Retinalyze A/S, Hørsholm, Denmark)
(Table 1). A comparison of published sensitivity and specific-
ity of each programme as well as a brief description of the
associated published studies are presented in Table 2.Direct
head-to-head comparisons between systems have proven dif-
ficult, mainly because of different photographic protocols, al-
gorithms and patient populations used for validation. A com-
mon thread amongst these automated systems is to identify
referable retinopathy: diabetic retinopathy, which requires the
attention of an ophthalmologist. To date, we have not
progressed to where human intervention can be fully removed
from such programmes. All of the systems described below
are semi-automated at some point in the workflow pathway
and require assistance of a human reader/grader.
iGradingM is a product of Medalytix Group Ltd, which received
its class 1 Conformité Européenne (CE) mark in 2013 and per-
forms disease/no diseasegrading for diabetic retinopathy [42,
43]. It was developed at the University of Aberdeen in Scotland
and uses previously published algorithms to assess both image
quality and disease and has been described in detail elsewhere
[32,44]. A previously trained automated classifier on a set of 35
images containing 198 individually annotated microaneurysm or
dot haemorrhages was used in its development.
It was first deployed on a large-scale population in the
Scottish Diabetic Retinopathy Screening Program in 2010,
after being validated using several large screening populations
in Scotland [39,43,45,46,47], the largest being 78,601
single-field 45° colour fundus images from 33,535 consecu-
tive patients [45]. In this retrospective study, 6.6 % of the
cohort had referable retinopathy, and iGradingM attained a
sensitivity of 97.8 % for referable retinopathy. Although the
specificity for referable retinopathy was not reported in the
paper, it has since been calculated at 41.1 % [20]. iGradingM
14 Page 4 of 9 Curr Diab Rep (2015) 15:14
has been further validated on 8271 screening episodes from a
South London population in the UK [48]. Here, there was a
higher percentage (7.1 %) of referable disease, and a sensitiv-
ity of 97.499.1 % and specificity of 98.399.3 % were
attained depending on the configuration used.
Network (Telemedical Retinal Image Analysis
and Diagnosis) was launched commercially in 2012 by its devel-
opers at Hubble Telemedical Inc., and was developed at the
University of Tennessee Health Science Center and the Oak
Ridge National Laboratory [49]. It consists of a web-based tech-
nical network infrastructure that has been functional as a telemed-
icine network for diabetic retinopathy screening in the mid-south
region of the USA since 2009 [50,51]. ARIA was introduced to
Network over a period of time, beginning with
image quality analysis at the point of image capture [52]and
most recently with the use of their patented content-based image
retrieval techniques for automated diagnosis [53]. The technique
of content-based image retrieval involves the comparison of im-
ages to large database collections using pictorial content. These
image features include various description models, perceptual
organisation, spatial relationships and may including clinical
metadata [53].
The authors themselves have reviewed challenges that they
have faced with validation of their ARIA software, due mostly to
the lack of large, varied and publically available datasets [50].
Briefly, they note that TRIAD
Network infrastructure has cre-
ated a useful internal validation mechanism by which new algo-
rithms can be developed and tested on datasets from different
time points [53,54,55]. Although external validation has been
performed for anomaly detection using publically available
datasets, these only contain a small number of images (<100),
and no large-scale validation has been published to date [50].
The IDx-DR was the first commercial product of IDx LLC,
Iowa, USA, that has in 2013 received CE approval as a Class
Tabl e 2 Efficiency of current ARIA systems for detecting referable retinopathy
ARIA system iGradingM [45]IDx-DR[65] RetmarkerDR [67]Retinalyze[76]
Sensitivity 0.98 (0.970.99) 0.97 (0.940.99) 0.96 (0.940.98) 0.97 (not provided)
Specificity 0.41 (not provided) 0.59 (55.763.0) 0.52 (0.500.53) 0.71 (not provided)
Data presented is sensitivity/specificity (95 % confidence interval) when available. Each of diabetic retinopathy grading programmes listed abovehave
been evaluated using different image set and grading methods and generally do not represent comparative performance. The difference in the perfor-
mance measures listed above may be due to difference in the image set, grading methods, algorithm threshold or a combination of various factors.
Referable retinopathy is defined as retinopathy more severe than mild nonproliferative diabetic retinopathy
Tabl e 1 Overview of current ARIA systems developed for diabetic retinopathy in telemedicine
ARIA system Company Developed at Grading details Current deployment
iGradingM Medalytix LLC; Digital
University of Aberdeen,
Scotland, UK
Diabetic retinopathy absent/
diabetic retinopathy present
CE mark 2013 as a class 1
medical device; Scottish
Diabetic Retinopathy
Screening Program
Hubble Telemedicine Inc. University of Tennessee
Health Science Center
and the Oak Ridge National
Laboratory, USA
CBIR technology not
approved for use; diagnosis
diabetic retinopathy severity
with a supervising retinal
Primary care services,
mid-south region of
the USA
IDx-DR IDx LLC University of Iowa, USA Diabetic retinopathy index;
referable retinopathy (more
than mild nonproliferative
diabetic retinopathy)
CE mark 2013 as a class IIa
medical device
RetmarkerDR Retmarker Ltd. Coimbra University,
Diabetic retinopathy absent/
diabetic retinopathy present;
microaneurysm turnover
CE mark 2011 as a Class IIa
medical device; diabetic
retinopathy screening
programmes in Portugal
RetinaLyze A/S RetinaLyze A/S Denmark Microaneurysm and
haemorrhage detection
CE mark 2011 as a class
IIa medical device;
software re-launched
commercially 2013
Curr Diab Rep (2015) 15:14 Page 5 of 9 14
lla medical device for sale in the European Economic Area
[56]. It utilises the Iowa Detection Program, which consists of
previously published algorithms including features such as
image quality assessment, detection of microaneurysms,
haemorrhage, cotton wool spots, and a fusion algorithm that
combines these analyses to produce the diabetic retinopathy
index [5761]. This index represents a dimensionless number
from 0 to 1 and represents the likelihood that the image con-
tains referable disease.
The IDx-DR has been validated in several large screening
populations [6264]. Most recently, the IDx-DR was validat-
ed on 1748 eyes with single-field 45° colour fundus images
acquired in French primary care clinics [65]. In this study, the
proportion of referable diabetic retinopathy was high at
21.7 % and sensitivity and specificity reported at 96.8 and
59.4 %, respectively.
RetmarkerDR was developed at Coimbra University, Portu-
gal, and has been used in screening programmes in Portugal
since 2011 [66]. It was launched commercially in 2011 by
Critical Health, which has since been renamed Retmarker
Ltd and attained CE approval as a Class lla medical device in
April of 2010. The main features of RetmarkerDR are its
image quality assessment algorithm, which has been validated
on publically available datasets [67], a co-registration algo-
rithm, which allows comparisons of the same location in the
retina to be made between visits [68,69].
The strength of the RetmarkerDR programme lies not in eval-
uating the severity of diabetic retinopathy but rather in predictive
longitudinal analysis using microaneurysm turnover. Two inde-
pendent prospective longitudinal studies using the RetmarkerDR
on single-field images have demonstrated the relationship of in-
creased microaneurysm turnover rates identified by the
RetmarkerDR programme and an increased rate for developing
clinically significant macular oedema [7072].
The Retinalyze System A/S (Retinalyze A/S, Hørsholm, Den-
mark) was developed in the late 1990s and combines image
quality assessment, with automated red microaneurysm and
haemorrhage lesion detection [73]. This system has been validat-
ed in two different patient populations in Denmark and Wales,
UK. In 2003, a Danish retrospective study of 260 eyes reported a
sensitivity of 93.1 % and specificity at 71.6 % in detecting reti-
nopathy [74]. A year later, a prospective validation study on 164
eyes from the same population, reported similar efficiencies, with
a sensitivity of 97.0 % and specificity of 75.0 % [75].
In 2003, a study on 200 eyes from the Welsh Community
Diabetic Retinopathy Study reported a sensitivity of 96.7 %
and specificity of 71.4 % [76]. Several years later (2008), a
modified version of the Retinalyze software (version,
described to be more sensitive to large red and bright le-
sions, was validated on 192 eyes from a similar population,
with a reported sensitivity of 93 % and specificity of 78 %
[77]. However, by then, Retinalyze was no longer commer-
cially available. More recently (2013), Retinalyze has been
reintroduced to the commercial market with updates to the
platform in keeping with advancements in computing plat-
forms. Retinalyze is currently available via the Cloud,there-
by allowing automated analysis via an internet connection
within 20 s of image upload.
Future Development of Retinal Image Analysis Approaches
The development of the ARIA algorithms typically requires a
trainingset of images where the computer programme is
taughtusing human-labelled sample images. The accompa-
nying limitations can be grouped into two categories. First is
the requirement of a large and varied dataset of training im-
ages. Due to regulatory and proprietary barriers, these datasets
are not readily available to researchers developing these algo-
rithms. However, two publicly accessible image data sets are
available, namely, the Methods for Evaluating Segmentation
and Indexing techniques Dedicated to Retinal Ophthalmology
(MESSIDOR) database, which is funded by the French Min-
istry of Research and Defense contains 1200 retinal images
with diagnosed level of diabetic retinopathy and macular oe-
dema [78], while the Retinopathy Online Challenge (ROC) by
the University of Iowa [79] contains 50 unique retinal images
that have been annotated for haemorrhages and/or
microaneurysms by a weighted consensus from four ophthal-
mologists. Second is the need for human-labelled images,
which can be labour intensive and costly. An intriguing ap-
proach to this problem utilises a Mechanical Turk approach
via a crowdsourcing internet market place. Proof-of-concept
studies suggest potential applicability for acquiring large
datasets of labelled images [80,81]. However, current studies
that have evaluated this approach have done so in a research
environment, and it should be emphasised that there are sig-
nificant limitations to this approach particularly when it is
used in an active clinical programme. A background of an
anonymous crowdsourced workforce is largely unknown,
and varying levels of potential bias may be introduced as
methods of standardisation would be limited. Furthermore,
ethical and privacy concerns remain a significant hurdle that
would prevent the release and online access of anonymised
clinical data. The data demonstrating effectiveness of using a
crowdsourced workforce is early and limited, but it provides
further evidence that the presence or absence of retinopathy or
referrable retinopathy can accurately be identified by nonphy-
sician graders provided that a standardised protocol is closely
followed. Should this approach be pursued in the future, rig-
orous quality control and quality assurance metrics need to be
14 Page 6 of 9 Curr Diab Rep (2015) 15:14
implemented but the actual grading of images may be pursued
in regions of the world wherein the cost efficiency of image
evaluation may be substantially reduced.
The application of ARIA to the infrastructure of telemedicine has
for the first time provided a strategy by which we may eventually
be able to address the enormous requirements of retinal exami-
nations needed in order to eliminate visual loss as a result of
diabetes. Like the extraordinary achievement of the WHO Small-
pox Eradication Program (19661980), this will require the col-
laboration of national health authorities, commercial companies
and the medical profession as a whole. The combination of
ARIA with telemedicine has the potential to substantially im-
prove the manner by which diabetes eye care is delivered, in
theory providing automated real-time patient evaluation, predic-
tive patient and population analyses and possible identification of
previously unrecognised novel markers of disease risk.
A major challenge to fully realising the potential of this tech-
nology remains the lack of a uniform validation for these sys-
tems. Current ARIA systems identify the presence of retinopathy
or the presence of referable retinopathy but do not identify the
specific levels of ETDRS severity from retinal images. A recent
collaborative effort between several international research groups
have published a list of recommendations for the validation of
ARIA algorithms and highlighted several key points, including
the need to focus on creating large repositories of real-life
datasets from international consortia, a multicentre approach to
create expert-annotated image datasets and an agreed-on perfor-
mance criteria to which different ARIA systems can be assessed
[82••]. Furthermore, critical to large-scale implementation of
ARIA systems for diabetic retinopathy is the involvement of
primary care providers on the frontlines of treating diabetes. Such
involvement will be important to promote management of blood
pressure, serum glucose, and lipid levels, all of which impact
diabetic retinopathy progression, while providing primary care
practitioners a simple action-oriented process to allow easy de-
tection of referable ocular disease and prompt access to appro-
priate eye care services.
Acknowledgments The research was supported by a grant from Fight
for Sight, UK (Grant number 1987), The Special Trustees of Moorfields
Eye Hospital and the National Institute for Health Research (NIHR) Bio-
medical Research Centre based at Moorfields Eye Hospital NHS Foun-
dation Trust and UCL Institute of Ophthalmology.
Compliance with Ethics Guidelines
Conflict of Interest Dawn A. Sim, Pearse A. Keane, Adnan Tufail,
Lloyd Paul Aiello and Paolo S. Silva declare that they have no conflict
of interest.
Catherine A. Egan reports grants and personal fees from Novartis. Her
husband is a retinal specialist and receives honoraria, grants and speakers
fees from academic and pharmaceutical industry relevant to this work.
Human and Animal Rights and Informed Consent This article does
not contain any studies with human or animal subjects performed by any
of the authors.
Papers of particular interest, published recently, have been
highlighted as:
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... However, the results obtained for the diagnosis of the diseased retina show widespread variations [19]. In recent years, there has been increasing interest in applying automation processes in ophthalmic telemedicine to reduce the need for screening professionals and to homogenize diagnostic criteria regardless of the origin and composition of the sample being evaluated [20]. For that reason, in this study, we assessed the use of the 2iRetinex software as a complement or substitute for the screening physician. ...
... EyeArt, Retmaker, iGrading or IDx are commercially available systems that have been used in teleophthalmic screening programs for DR diagnosis [20]. The functionality of iGrading is comparable to that of 2iRetinex by combining an image quality system and a DR identification criterion. ...
Full-text available
Background: Retinopathy is the most common microvascular complication of diabetes mellitus. It is the leading cause of blindness among working-aged people in developed countries. The use of telemedicine in the screening system has enabled the application of large-scale population-based programs for early retinopathy detection in diabetic patients. However, the need to support ophthalmologists with other trained personnel remains a barrier to broadening its implementation. Methods: Automatic diagnosis of diabetic retinopathy was carried out through the analysis of retinal photographs using the 2iRetinex software. We compared the categorical diagnoses of absence/presence of retinopathy issued by family physicians (PCP) with the same categories provided by the algorithm (ALG). The agreed diagnosis of three specialist ophthalmologists is used as the reference standard (OPH). Results: There were 653 of 3520 patients diagnosed with diabetic retinopathy (DR). Diabetic retinopathy threatening to vision (STDR) was found in 82 patients (2.3%). Diagnostic sensitivity for STDR was 94% (ALG) and 95% (PCP). No patient with proliferating or severe DR was misdiagnosed in both strategies. The k-value of the agreement between the ALG and OPH was 0.5462, while between PCP and OPH was 0.5251 (p = 0.4291). Conclusions: The diagnostic capacity of 2iRetinex operating under normal clinical conditions is comparable to screening physicians.
... Image processing segmentation combines algorithms that identify, refine, and extract features of interest from CF images. These findings may provide insight into using retinal vascular changes as evaluated by VESGEN to diagnosis retinal diseases earlier and may be adapted for use in telemedicine [28]. ...
Full-text available
(1) Background: Retinal vascular imaging plays an essential role in diagnosing and managing chronic diseases such as diabetic retinopathy, sickle cell retinopathy, and systemic hypertension. Previously, we have shown that individuals with pulmonary arterial hypertension (PAH), a rare disorder, exhibit unique retinal vascular changes as seen using fluorescein angiography (FA) and that these changes correlate with PAH severity. This study aimed to determine if color fundus (CF) imaging could garner identical retinal information as previously seen using FA images in individuals with PAH. (2) Methods: VESGEN, computer software which provides detailed vascular patterns, was used to compare manual segmentations of FA to CF imaging in PAH subjects (n = 9) followed by deep learning (DL) processing of CF imaging to increase the speed of analysis and facilitate a noninvasive clinical translation. (3) Results: When manual segmentation of FA and CF images were compared using VESGEN analysis, both showed identical tortuosity and vessel area density measures. This remained true even when separating images based on arterial trees only. However, this was not observed with microvessels. DL segmentation when compared to manual segmentation of CF images showed similarities in vascular structure as defined by fractal dimension. Similarities were lost for tortuosity and vessel area density when comparing manual CF imaging to DL imaging. (4) Conclusions: Noninvasive imaging such as CF can be used with VESGEN to provide an accurate and safe assessment of retinal vascular changes in individuals with PAH. In addition to providing insight into possible future clinical translational use.
... At the end, the mixture of these features introduces type and stage of DR. Different CAD systems have been presented in state of arts for early DR detection and related lesions [12,18,19,[70][71][72][73][74][75][76][77]. ...
Full-text available
Diabetes Mellitus (DM) can lead to significant microvasculature disruptions that eventually causes diabetic retinopathy (DR), or complications in the eye due to diabetes. If left unchecked, this disease can increase over time and eventually cause complete vision loss. The general method to detect such optical developments is through examining the vessels, optic nerve head, microaneurysms, haemorrhage, exudates, etc. from retinal images. Ultimately this is limited by the number of experienced ophthalmologists and the vastly growing number of DM cases. To enable earlier and efficient DR diagnosis, the field of ophthalmology requires robust computer aided diagnosis (CAD) systems. Our review is intended for anyone, from student to established researcher, who wants to understand what can be accomplished with CAD systems and their algorithms to modeling and where the field of retinal image processing in computer vision and pattern recognition is headed. For someone just getting started, we place a special emphasis on the logic, strengths and shortcomings of different databases and algorithms frameworks with a focus on very recent approaches.
... Recently, automated retinal image analysis (ARIA) systems have been developed for the diagnosis of complex diseases such as diabetic retinopathy and glaucoma (Sim et al., 2015;. The development of these ARIA systems involved ML-based methods to detect structural changes determined with optical coherence tomography (OCT) imaging resulting in high analytical accuracy in automatically classifying disease phenotypes based on structural characteristics (Zhu et al., 2014;Asaoka et al., 2016;An et al., 2019). ...
Full-text available
PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.Conclusions The present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.
... Other studies describe that high-resolution photos generate more details of injuries and help with classification, [29] but other reports do not demonstrate this relationship and only raise the threshold beyond which diagnostic precision does not improve. [30][31][32] To improve performance, it would be advisable to train algorithms and find a minimum requirement for image resolution and sample size associated with staff training. [33,34] Regarding level of damage, a referable case was considered when there was a DR worse than mild according to the ICDR scale [3,24,24] and corresponded to level 35 or higher of the early treatment study of DR. ...
Diabetic retinopathy (DR) is the leading cause of blindness among working-age persons in high-income countries. A public system strategy was developed to improve screening, using telemedicine, automatic detection using artificial intelligence (A/I) and medical reporting. In the current work, we evaluated program efficiency. Material: We conducted a cross-sectional study using information from an institutional database of retinographies submitted to the A/I platform in 2019. With a positive test, a medical report was made using the international scale. Results: In 2019, 220,994 retinographies were reported, corresponding to 24.0% of diabetic patients. Around half (53.0%) of cases were discarded by A/I, being different in each regional health service. The medical analysis discarded diabetic retinopathy in 30.2% of exams, 11.5% had diabetic retinopathy, including 2.3% with risk of blindness, while 3.7% could not be evaluated. Discussion: The use of A/I allowed optimizing the medical resources, discarded 53% of cases, which helped in the screening of diabetic retinopathy. Coverage is still insufficient, and detection of macular edema must be improved.
... Multiple automated algorithms for DR detection from retinal colour photographs have been developed [7,[10][11][12]. IDx-DR was the first autonomous artificial intelligence (AI)-based diagnostic system approved by the U.S. Food and Drug Administration (FDA). ...
Full-text available
Introduction: Comparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an FDA-approved screening system and human retina specialists' gradings from ultra-widefield (UWF) colour images. Methods: Asymptomatic diabetics without a previous diagnosis of DR were included in this prospective observational pilot study. Patients were imaged with autonomous AI (IDx-DR, Digital Diagnostics). For each eye, two 45° colour fundus images were analysed by a secure server-based AI algorithm. UWF colour fundus imaging was performed using Optomap (Daytona, Optos). The International Clinical DR severity score was assessed both on a 7-field area projection (7F-mask) according to the early treatment diabetic retinopathy study (ETDRS) and on the total gradable area (UWF full-field) up to the far periphery on UWF images. Results: Of 54 patients included (n = 107 eyes), 32 were type 2 diabetics (11 females). Mean BCVA was 0.99 ± 0.25. Autonomous AI diagnosed 16 patients as negative, 28 for moderate DR and 10 for having a vision-threatening disease (severe DR, proliferative DR, diabetic macular oedema). Based on the 7F-mask grading with the eye with the worse grading defining the DR stage 23 patients were negative for DR, 11 showed mild, 19 moderate and 1 severe DR. When UWF full-field was analysed, 20 patients were negative for DR, while the number of mild, moderate and severe DR patients were 12, 21, and 1, respectively. Conclusions: The autonomous AI-based DR examination demonstrates sufficient accuracy in diagnosing asymptomatic non-proliferative diabetic patients with referable DR even compared to UWF imaging evaluated by human experts offering a suitable method for DR screening.
... In recent times, the entry of artificial intelligence (AI) algorithms further provides immediate grading and feedback on fundus photographs acquired by trained personnel in an out-of-hospital location (including primary care clinics and pharmacies) [21-23]. These AI-backed systems feature automated retinal image Diabetic Eye Disease -From Therapeutic Pipeline to the Real World 4 analysis (ARIA) [24,25]. The image to be graded or analyzed can be acquired using digital fundus cameras, and now even handheld mobile devices, including smartphones, can be used. ...
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Diabetic macular edema is a complication of diabetes mellitus (DM) which contributes significantly to the burden of visual impairment amongst persons living with diabetes. Chronic hyperglycemia triggers a cascade of pathologic changes resulting in breakdown of the retinal blood barrier. Understanding the pathophysiological and biochemical changes occurring in diabetes has led to developing novel therapeutics and effective management strategies for treating DME. The clinical utility of optical coherence tomography (OCT) imaging of the retina provides a detailed assessment of the retina microstructure, valid for individualization of patient treatment and monitoring response to treatment. Similarly, OCT angiography (dye-less angiography), another innovation in imaging of DME, provides an understanding of retinal vascula-ture in DME. From the earlier years of using retinal laser photocoagulation as the gold standard for treating DME, to the current use of intravitreal injection of drugs, several clinical trials provided evidence on safety and efficacy for the shift to intravitreal ste-roids and anti-vascular endothelial growth factor use. The short durability of available drugs leading to frequent intravitreal injections and frequent clinic visits for monitoring constitute an enormous burden. Therefore, extended durability drugs are being designed, and remote monitoring of DME may be a solution to the current challenges.
Purpose: Studies have indicated that the observed association between vitamin D and myopia was confounded by time spent outdoors. This study aimed to elucidate this association using a national cross-sectional dataset. Methods: Participants with 12 to 25 years who participated in non-cycloplegic vision exam from National Health and Nutrition Examination Survey (NHANES) 2001 to 2008 were included in the present study. Myopia was defined as spherical equivalent of any eyes ≤ -0.5 diopters (D). Results: 7,657 participants were included. The weighted proportion of emmetropes, mild myopia, moderate myopia, and high myopia were 45.5%, 39.1%, 11.6%, and 3.8%, respectively. After adjusting for age, gender, ethnicity, TV/computer usage, and stratified by education attainment, every 10 nmol/L increment of serum 25(OH)D concentration was associated with a reduced risk of myopia (odds ratio [OR] = 0.96, 95% confidence interval [95%CI] 0.93-0.99 for any myopia; OR = 0.96, 95%CI 0.93-1.00 for mild myopia; OR = 0.99, 95%CI 0.97-1.01 for moderate myopia; OR = 0.89, 95%CI 0.84-0.95 for high myopia). Serum 25(OH)D level was closely correlated with time spent outdoors. After categorizing time spent outdoors into quarters (low, low-medium, medium-high, and high), every 1 quarter increment of time spent outdoors was associated with 2.49 nmol/L higher serum 25(OH)D concentration. After adjusting for time spent outdoors, serum 25(OH)D level did not show significant association with myopia (OR = 1.01, 95%CI 0.94-1.06 for 10 nmol/L increment). Conclusions: The association between high serum vitamin D and reduced risk of myopia is confounded by longer time spent outdoors. Evidence from the present study does not support that there is a direct association between serum vitamin D level with myopia.
The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy.
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To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.
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Purpose: To report 36-month outcomes of RIDE (NCT00473382) and RISE (NCT00473330), trials of ranibizumab in diabetic macular edema (DME). Design: Phase III, randomized, multicenter, double-masked, 3-year trials, sham injection-controlled for 2 years. Participants: Adults with DME (n=759), baseline best-corrected visual acuity (BCVA) 20/40 to 20/320 Snellen equivalent, and central foveal thickness (CFT) ≥ 275 μm on optical coherence tomography. Methods: Patients were randomized equally (1 eye per patient) to monthly 0.5 mg or 0.3 mg ranibizumab or sham injection. In the third year, sham patients, while still masked, were eligible to cross over to monthly 0.5 mg ranibizumab. Macular laser was available to all patients starting at month 3; panretinal laser was available as necessary. Main outcome measures: The proportion of patients gaining ≥15 Early Treatment Diabetic Retinopathy Study letters in BCVA from baseline at month 24. Results: Visual acuity (VA) outcomes seen at month 24 in ranibizumab groups were consistent through month 36; the proportions of patients who gained ≥15 letters from baseline at month 36 in the sham/0.5 mg, 0.3 mg, and 0.5 mg ranibizumab groups were 19.2%, 36.8%, and 40.2%, respectively, in RIDE and 22.0%, 51.2%, and 41.6%, respectively, in RISE. In the ranibizumab arms, reductions in CFT seen at 24 months were, on average, sustained through month 36. After crossover to 1 year of treatment with ranibizumab, average VA gains in the sham/0.5 mg group were lower compared with gains seen in the ranibizumab patients after 1 year of treatment (2.8 vs. 10.6 and 11.1 letters). Per-injection rates of endophthalmitis remained low over time (∼0.06% per injection). The incidence of serious adverse events potentially related to systemic vascular endothelial growth factor inhibition was 19.7% in patients who received 0.5 mg ranibizumab compared with 16.8% in the 0.3 mg group. Conclusions: The strong VA gains and improvement in retinal anatomy achieved with ranibizumab at month 24 were sustained through month 36. Delayed treatment in patients receiving sham treatment did not seem to result in the same extent of VA improvement observed in patients originally randomized to ranibizumab. Ocular and systemic safety was generally consistent with the results seen at month 24. Financial disclosure(s): Proprietary or commercial disclosure may be found after the references.
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To report on the causes of blindness certifications in England and Wales in working age adults (16-64 years) in 2009-2010; and to compare these with figures from 1999 to 2000. Analysis of the national database of blindness certificates of vision impairment (CVIs) received by the Certifications Office. Working age (16-64 years) population of England and Wales. Number and cause of blindness certifications. The Certifications Office received 1756 CVIs for blindness from persons aged between 16 and 64 inclusive between 1 April 2009 and 31 March 2010. The main causes of blindness certifications were hereditary retinal disorders (354 certifications comprising 20.2% of the total), diabetic retinopathy/maculopathy (253 persons, 14.4%) and optic atrophy (248 persons, 14.1%). Together, these three leading causes accounted for almost 50% of all blindness certifications. Between 1 April 1999 and 31 March 2000, the leading causes of blindness certification were diabetic retinopathy/maculopathy (17.7%), hereditary retinal disorders (15.8%) and optic atrophy (10.1%). For the first time in at least five decades, diabetic retinopathy/maculopathy is no longer the leading cause of certifiable blindness among working age adults in England and Wales, having been overtaken by inherited retinal disorders. This change may be related to factors including the introduction of nationwide diabetic retinopathy screening programmes in England and Wales and improved glycaemic control. Inherited retinal disease, now representing the commonest cause of certification in the working age population, has clinical and research implications, including with respect to the provision of care/resources in the NHS and the allocation of research funding.
Purpose: To evaluate diabetic retinopathy (DR) progression in patients with diabetes mellitus type 2 in 2 populations of different ethnicity. Methods: A prospective observational study was designed to follow eyes/patients with mild nonproliferative DR, for 2 years or until the development of central-involved macular edema (CIME), in 2 centers from different regions of the world. A total of 205 eyes/patients fulfilled the inclusion/exclusion criteria and were included in this study. Ophthalmological examinations, fundus photography with RetmarkerDR analysis, and optical coherence tomography were performed at baseline and at 6, 12 and 24 months. Results: Of the 158 eyes/patients that completed this study, 24 eyes developed CIME and 134 eyes were present at the last study visit. Eighty-eight eyes (56.4%) were classified as phenotype A, 49 (31.4%) as phenotype B, and 19 (12.2%) as phenotype C. Phenotype A is associated with a very low risk for development of CIME in comparison with phenotypes B and C. The OR for development of CIME was 19.0 for phenotype B and 25.1 for phenotype C. Conclusion: Eyes in the initial stages of DR show different phenotypes with different risks of progression to ME. The phenotypes associated with increased risks of progression show different distributions in patients of different ethnicities.
The modified Airlie House classification of diabetic retinopathy has been extended for use in the Early Treatment Diabetic Retinopathy Study (ETDRS). The revised classification provides additional steps in the grading scale for some characteristics, separates other characteristics previously combined, expands the section on macular edema, and adds several characteristics not previously graded. The classification is described and illustrated and its reproducibility between graders is assessed by calculating percentages of agreement and kappa statistics for duplicate gradings of baseline color nonsimultaneous stereoscopic fundus photographs. For retinal hemorrhages and/ or microaneurysms, hard exudates, new vessels, fibrous proliferations, and macular edema, agreement was substantial (weighted kappa, 0.61 to 0.80). For soft exudates, intraretinal microvascular abnormalities, and venous beading, agreement was moderate (weighted kappa, 0.41 to 0.60). A double grading system, with adjudication of disagreements of two or more steps between duplicate gradings, led to some improvement in reproducibility for most characteristics.
The Early Treatment Diabetic Retinopathy Study (ETDRS) enrolled 3711 patients with mild-to-severe nonproliferative or early proliferative diabetic retinopathy in both eyes. One eye of each patient was assigned randomly to early photocoagulation and the other to deferral of photocoagulation. Followup examinations were scheduled at least every 4 months and photocoagulation was initiated in eyes assigned to deferral as soon as high-risk proliferative retinopathy was detected. Eyes selected for early photocoagulation received one of four different combinations of scatter (panretinal) and focal treatment. This early treatment, compared with deferral of photocoagulation, was associated with a small reduction in the incidence of severe visual loss (visual acuity less than 5/200 at two consecutive visits), but 5-year rates were low in both the early treatment and deferral groups (2.6% and 3.7%, respectively). Adverse effects of scatter photocoagulation on visual acuity and visual field also were observed. These adverse effects were most evident in the months immediately following treatment and were less in eyes assigned to less extensive scatter photocoagulation. Provided careful follow-up can be maintained, scatter photocoagulation is not recommended for eyes with mild or moderate nonproliferative diabetic retinopathy. When retinopathy is more severe, scatter photocoagulation should be considered and usually should not be delayed if the eye has reached the high-risk proliferative stage. The ETDRS results demonstrate that, for eyes with macular edema, focal photocoagulation is effective in reducing the risk of moderate visual loss but that scatter photocoagulation is not. Focal treatment also increases the chance of visual improvement, decreases the frequency of persistent macular edema, and causes only minor visual field losses. Focal treatment should be considered for eyes with macular edema that involves or threatens the center of the macula.
• In a population-based study in southern Wisconsin, 996 insulin-taking, younger-onset diabetic persons were examined using standard protocols to determine the prevalence and severity of diabetic retinopathy and associated risk variables. The prevalence of diabetic retinopathy varied from 17% to 97.5% in persons with diabetes for less than five years and 15 or more years, respectively. Proliferative retinopathy varied from 1.2% to 67% in persons with diabetes for less than ten years and 35 or more years, respectively. For persons with diabetes of 10 years' duration or less, the Cox regression model relates the severity of retinopathy to longer duration, older age at examination, and higher levels of glycosylated hemoglobin. After ten years of diabetes, severity of retinopathy was related to longer duration, high levels of glycosylated hemoglobin, presence of proteinuria. higher diastolic BP, and male sex.
Objective: To determine the prevalence of diabetic retinopathy among adults 40 years and older in the United States. Methods: Pooled analysis of data from 8 population-based eye surveys was used to estimate the prevalence, among persons with diabetes mellitus (DM), of retinopathy and of vision-threatening retinopathy-defined as proliferative or severe nonproliferative retinopathy and/or macular edema. Within strata of age, race/ethnicity, and gender, US prevalence rates were estimated by multiplying these values by the prevalence of DM reported in the 1999 National Health Interview Survey and the 2000 US Census population. Results: Among an estimated 10.2 million US adults 40 years and older known to have DM, the estimated crude prevalence rates for retinopathy and vision-threatening retinopathy were 40.3% and 8.2%, respectively. The estimated US general population prevalence rates for retinopathy and vision-threatening retinopathy were 3.4% (4.1 million persons) and 0.75% (899000 persons). Future projections suggest that diabetic retinopathy will increase as a public health problem, both with aging of the US population and increasing age-specific prevalence of DM over time. Conclusion: Approximately 4.1 million US adults 40 years and older have diabetic retinopathy; 1 of every 12 persons with DM in this age group has advanced, vision-threatening retinopathy.
purpose. To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. methods. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. results. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). conclusions. Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.
• In a population-based study in southern Wisconsin, 1,370 patients given diagnoses of diabetes at age 30 years or older were examined using standard protocols to determine the prevalence and severity of diabetic retinopathy and associated risk variables. The prevalence of diabetic retinopathy varied from 28.8% in persons who had diabetes for less than five years to 77.8% in persons who had diabetes for 15 or more years. The rate of proliferative diabetic retinopathy varied from 2.0% in persons who had diabetes for less than five years to 15.5% in persons who had diabetes for 15 or more years. By using the Cox regression model, the severity of retinopathy was found to be related to longer duration of diabetes, younger age at diagnosis, higher glycosylated hemoglobin levels, higher systolic BP, use of insulin, presence of proteinuria, and small body mass.
Purpose: A head-to-head comparison was performed between vascular endothelial growth factor blockade and laser for treatment of diabetic macular edema (DME). Design: Two similarly designed, double-masked, randomized, phase 3 trials, VISTA(DME) and VIVID(DME). Participants: We included 872 patients (eyes) with type 1 or 2 diabetes mellitus who presented with DME with central involvement. Methods: Eyes received either intravitreal aflibercept injection (IAI) 2 mg every 4 weeks (2q4), IAI 2 mg every 8 weeks after 5 initial monthly doses (2q8), or macular laser photocoagulation. Main outcome measures: The primary efficacy endpoint was the change from baseline in best-corrected visual acuity (BCVA) in Early Treatment Diabetic Retinopathy Study (ETDRS) letters at week 52. Secondary efficacy endpoints at week 52 included the proportion of eyes that gained ≥ 15 letters from baseline and the mean change from baseline in central retinal thickness as determined by optical coherence tomography. Results: Mean BCVA gains from baseline to week 52 in the IAI 2q4 and 2q8 groups versus the laser group were 12.5 and 10.7 versus 0.2 letters (P < 0.0001) in VISTA, and 10.5 and 10.7 versus 1.2 letters (P < 0.0001) in VIVID. The corresponding proportions of eyes gaining ≥ 15 letters were 41.6% and 31.1% versus 7.8% (P < 0.0001) in VISTA, and 32.4% and 33.3% versus 9.1% (P < 0.0001) in VIVID. Similarly, mean reductions in central retinal thickness were 185.9 and 183.1 versus 73.3 μm (P < 0.0001) in VISTA, and 195.0 and 192.4 versus 66.2 μm (P < 0.0001) in VIVID. Overall incidences of ocular and nonocular adverse events and serious adverse events, including the Anti-Platelet Trialists' Collaboration-defined arterial thromboembolic events and vascular deaths, were similar across treatment groups. Conclusions: At week 52, IAI demonstrated significant superiority in functional and anatomic endpoints over laser, with similar efficacy in the 2q4 and 2q8 groups despite the extended dosing interval in the 2q8 group. In general, IAI was well-tolerated.