·Meta-Analysis·
Current
applications of machine learning in the screening and diagnosis of glaucoma: a
systematic review and Meta-analysis
Patrick
Murtagh1, Garrett Greene2, Colm O'Brien1
1Department of Ophthalmology, Mater
Misericordiae University Hospital, Eccles Street, Dublin D07 R2WY, Ireland
2RCSI Education and Research Centre,
Beaumont Hospital, Dublin D05 AT88, Ireland
Correspondence to: Patrick Murtagh. 123 Melvin
Road, Terenure, Dublin D6W FN29, Ireland. murtagp@tcd.ie
Received:
Abstract
AIM: To compare the
effectiveness of two well described machine learning modalities, ocular
coherence tomography (OCT) and fundal photography, in terms of diagnostic
accuracy in the screening and diagnosis of glaucoma.
METHODS: A systematic search of
Embase and PubMed databases was undertaken up to 1st of February
2019. Articles were identified alongside their reference lists and relevant
studies were aggregated. A Meta-analysis of diagnostic accuracy in terms of
area under the receiver operating curve (AUROC) was performed. For the studies
which did not report an AUROC, reported sensitivity and specificity values were
combined to create a summary ROC curve which was included in the Meta-analysis.
RESULTS: A total of 23 studies
were deemed suitable for inclusion in the Meta-analysis. This included 10
papers from the OCT cohort and 13 from the fundal photos cohort. Random effects
Meta-analysis gave a pooled AUROC of 0.957 (95%CI=0.917 to 0.997) for fundal
photos and 0.923 (95%CI=0.889 to 0.957) for the OCT cohort. The slightly higher
accuracy of fundal photos methods is likely attributable to the much larger
database of images used to train the models (59 788 vs 1743).
CONCLUSION: No demonstrable
difference is shown between the diagnostic accuracy of the two modalities. The
ease of access and lower cost associated with fundal photo acquisition make
that the more appealing option in terms of screening on a global scale, however
further studies need to be undertaken, owing largely to the poor study quality
associated with the fundal photography cohort.
KEYWORDS: machine learning;
glaucoma; ocular coherence tomography; fundal photography; diagnosis;
Meta-analysis
DOI:10.18240/ijo.2020.01.22
Citation:
Murtagh P, Greene G, O’Brien C. Current applications of machine learning in the
screening and diagnosis of glaucoma: a systematic review and Meta-analysis. Int
J Ophthalmol 2020;13(1):149-162
INTRODUCTION
Glaucoma is a term used to describe a group of optic
neuropathies which cause damage to retinal ganglion cells, and is the second
leading cause of permanent blindness in developed countries[1].
The damage caused by glaucoma is irreversible and therefore early detection and
treatment is vital to halt visual damage[2]. From
a global perspective, the number of people diagnosed with the disease is
expected to almost double from 76 million in 2020 to 112 million in 2040[3]. Glaucoma is usually related to an increase in
intraocular pressure which leads to stress induced damage on the retinal
ganglion cells resulting in a characteristic appearance of the optic nerve head
and associated visual field defects[4]. Pressure
lowering medications and/or surgery can be used to halt its progression,
especially if it is detected at an early stage. However, the disease has an
insidious onset and so therefore patients can remain asymptomatic for many
years before they attend for investigation and/or treatment. Early detection is
essential to ensure patients continue to have an adequate quality of life and
allow them to retain their independence and the ability to drive[5]. The economic and social impact that glaucomatous optic
neuropathy can have on society has been well described[6].
The pathogenesis and progression of glaucoma is still poorly understood[7].
Currently there are numerous methods used to diagnose and
screen for glaucoma, however these techniques are expensive, time consuming,
require skilled operators and are manual[8]. Four
modalities are routinely used; perimetry to detect a visual field defect,
pachymetry to detect corneal thickness, tonometry to measure intraocular
pressure and fundoscopy to examine the optic nerve head. Glaucoma is a disease
that is seen to increase significantly with advancing age[9]
and the projected increase in the population over the age of 50 is expected to
double over the next 20y[10]. It is therefore
imperative that an efficient screening and/or diagnosing system is established
to halt both the disease burden and the burden on ophthalmic departments.
Glaucomatous optic neuropathy can result in a thinning of
the retinal nerve fibre layer (RNFL) and an associated enlargement of the
cup-to-disc ratio (CDR). Peripapillary atrophy is also a well-known sign
associated with glaucoma[11], however this can be
seen in other ocular pathologies such as high myopia[12].
Confusingly, individuals with myopia have an increased risk of developing
glaucoma[13].
The Anderson Patella criteria is the gold standard
criteria for manifest glaucoma diagnosis. It is stated for a diagnosis to be
made, the following must be seen on a 30-2 Humphrey visual field test (Humphrey
Field Analyser, Carl Zeiss Meditec, Dublin, California): 1) abnormal glaucoma
hemi-field test, 2) three or more non-edge points, which are contiguous, must
be depressed with a P<5% with at least one of these having a P<1%,
3) this must be demonstrated on two or more field tests[14].
Ocular coherence tomography (OCT) is a non-invasive
imaging technique which provides micrometre resolution cross sectional views of
the retina[15]. It can be utilised to assess RNFL
thinning around the optic nerve head and macular area. Fundal photography is
another imaging technique which takes photographs of the inner retina mainly
using a widefield fundus camera. Parameters including the size of the optic
nerve head and the CDR, alongside peripapillary atrophy, vessel branching and
tortuosity can be examined using fundal photos[16].
OCT scanners can interpret these parameters alongside RNFL thickness. OCT scans
are accurate, reproducible and are not patient dependant. They can supply us
with information about change in thickness of the RNFL and can be used in
differentiate glaucomatous from non-glaucomatous eyes[17].
RNFL thickness as determined by OCT scans has shown a high correlation with the
functional status of the optic nerve[18].
Fundal photos[19] and OCT
scanning techniques[20] have proven to be useful
screening and diagnosing modalities for glaucoma. A contrast between the two
modalities exist relating to the ease of access and speed of acquisition of
fundal photos in comparison to cost and user expertise associated with OCT
scanning. The use of a fundal camera negates the price associated with more
expensive diagnostic equipment or in setting where one is just not available (e.g.,
lower income countries). The average cost of an OCT scanner is approximately
$40 000 whereas one can augment their smartphone with a lens to aid in the
acquisition of basic fundal photos[21]. Fundal
photos can be used concomitantly to diagnose other ocular pathologies such as
age related macular degeneration (ARMD) or diabetic retinopathy (DR)[22].
Artificial intelligence (AI), in particular “Machine
learning”, has seen a recent upsurge, particularly its use in medicine, namely
ophthalmology, and is currently being developed as a screening and diagnostic
tool in many ophthalmic conditions. Machine learning refers to any process in
which an algorithm is iteratively improved or “trained” in performing a task,
usually a classification or identification task, by repeated exposure to many
examples, known as the training data or training set. The trained algorithm can
then be tested by measuring its performance in classifying novel unseen data
(the test set).
In particular, “supervised” learning algorithms have
proven highly successful in automating binary classification tasks, such as
determining the presence or absence of a pathology. “Supervised learning”
refers to training regimes in which the algorithm is given both the input (e.g.,
an OCT or fundus image) and the correct output (e.g., the correct
diagnosis) for each element in the training set. In this way, the algorithm
implicitly learns a mathematical function which maps each input to the correct
output. If new data is applied to this function, the machine learning algorithm
should be able to classify it correctly. Machine learning can be utilised when
we can’t directly express how a problem should be solved using an algorithm but
we can illustrate to the machine examples that are both positive and negative
and allow it to identify a function for itself. As a result, the validity of a
machine learning algorithm depends heavily on the size and quality of the
training data, and so validation of algorithms is highly important to ensure
that the results will generalise. Steps for constructing an AI model include
pre-processing the raw data, training the model, validating it and then testing
it[23].
Machine Learning Algorithms
Artificial neural networks Artificial neural network (ANN) models are inspired by
the structure of the brain, in particular the human visual system, making them
highly useful in automated image analysis. ANNs consists of many simple
simulated processing units (“neurons”) connected in one or more layers. Neurons
receive input from preceding layers, combine the inputs according to simple
summation rules, and generate an output which is fed forward to the next layer.
The lowest layer of the network represents the input (e.g., image pixel
values), while the final layer represents the output or classification. Inputs
from one neuron to another are “weighted”, with values analogous to synaptic
weights in neural connections. As the algorithm is trained, these weights are
updated according to simple feedback rules to improve the accuracy of the
classification.
Support vector machine Support vector machines (SVMs) apply a multi-dimensional
transform to the input data (image pixels). The algorithm then attempts to
identify the hyperplane in this higher-dimensional space which best separates
the training data into the desired categories (e.g., glaucomatous and
non-glaucomatous). The further away the data points lie form the plane, the
more confident the model is that it identified them correctly[24]. The algorithm’s objective is to find the plane with
the greatest margin, i.e., the greatest distance away from points and
the plane so that it can achieve the greatest accuracy.
Random forest
Random forest (RAN) uses multiple
non-correlated decision trees. Each decision tree predicts an output and the
output with the highest prediction rate is the one which has the greatest
likelihood to be correct. In essence, the outcome which gets the greatest
number of “votes” from multiple non-correlated prediction models is the answer
which is presumed to be the most accurate[25].
K-nearest neighbour
This is an algorithm that works
on the principal that data with similar characteristics will lie in close
proximity to each other. For a new piece of data, the algorithm determines how
close it lies in relation to another piece of predesignated data and will then
make an assumption on whether the new data has a positive or negative value[26].
Validation Validation is a process in which the trained model is
evaluated with a testing data set. It is used to determine how well the
algorithm can classify images that it has never seen before. Cross validation
is a commonly utilised method of testing the validity of machine learning
algorithms to reduce the risk of overfitting. Overfitting is when a machine
learning algorithm learns the details and noise in a testing set too well and
subsequently impacts negatively on the classification of future data[27]. The most commonly applied cross-validation method is
“K-fold cross-validation”. In this method, the dataset is randomly split into k
subsets of equal size. Common choices of k are 5 or 10 (5-fold or 10-fold cross
validation). The training is then performed using k-1 of the subsets as the
training set, and the remaining 1 subset as the test set. This process is
repeated k times, leaving a different subset out of the training each time. The
final estimate of the model’s accuracy is given by pooling the results of the
validation in each of the k subsets. Although k-fold cross-validation uses the
same data for both training and testing, an individual data point is never
included in both the training and test sets for a particular iteration,
reducing the likelihood of overfitting. This model has proven to be effective
to avoid the overfitting or under fitting of data[28].
It has been stated that cross validation is a better method for testing and
training than random allocation[29]. Random
allocation is when the training and validation set are randomly split, with one
section used for training and another used for validation.
Ophthalmology and machine learning Machine learning is a technology that is still in its
embryonic stage. Deep learning (a subset of machine learning focusing on ANN),
which only found its feet in the 2000s, is a technology with widespread use in
modern society including speech recognition, real time language translation
and, most notably, image recognition[30]. Its
transition to medical imaging analysis was an obvious step.
Ophthalmology is an ideal specialty for the implantation
of machine learning due to the ability to obtain high resolution images of the
posterior of the eye in the form of fundal photos or OCT scans. These are
non-invasive techniques with no radiation or potential for harm. A recent study[31] examining the ability of a machine learning algorithm
to identify referable retinal diseases from OCT scans revealed that its success
was comparable with clinical retinal specialists. It demonstrated that it could
work in a real-world setting, with the benefit of being able to diagnose
multiple pathologies.
Multiple machine learning parameters are currently being
assessed by numerous authors to aid in glaucoma diagnosis. There is debate over
which screening frame work is the most sensitive and/or specific. On review of
the literature, the most utilised screening modalities include either fundal
photographs (which incorporates optic nerve head assessment and retinal
vascular geometry) or OCT imaging. As previously stated, techniques offer the
advantage of quick assessment time, however the OCT scanner is a significantly
more expensive than the fundal camera and requires a skilled operator.
The literature has indicated that there are many studies
examining the efficacy of these modalities in the screening/diagnosing of
glaucoma, but none have compared the sensitivity and specificity of these tests
in comparison to one another. To facilitate the mass screening of glaucoma, it
would be beneficial to identify the most appropriate diagnostic test and to
date the literature has failed to examine this.
Study aims and objectives To determine the diagnostic accuracy, in terms of
sensitivity and specificity and/or area under the receiver operating curve
(AUROC), of machine learning (including, but not restricted to, SVM, ANN,
convolutional neural network (CNN), K-nearest neighbour, least square SVM
(LS-SVM), naïve Bayes and sequential minimal optimisation in diagnosing
glaucoma and identifying those at risk. The two imaging modalities to be
examined are fundal photographs of the optic disc, retinal vessels and OCT
imaging.
These will be compared to the current reference test
which is defined by the Anderson Patella criteria[14]
for glaucoma diagnosis. Referable glaucomatous optic neuropathy is a term used
when there is an increased CDR and therefore an associated suspicion of
glaucoma. Not all suspicious discs are glaucomatous and a proportion of the
normal population will have an increased CDR of greater than 0.7[32]. Functional damage, as represented by specific loss
of peripheral visual field, has been demonstrated to be the most crucial
component in the diagnosis of glaucoma[33].
In essence, we hope to elucidate from the general
population (Population) which method of machine learning screening for glaucoma
(Experimental test) is most accurate when we compare it to the gold standard
test which is perimetry as defined by the Anderson Patella criteria (Reference
test). This will allow us to determine the most appropriate imaging modality to
utilise in terms of the automation of the mass screening of glaucoma.
MATERIALS AND METHODS
Search Strategy
A search of Pubmed and Embase was
undertaken up to the first of February 2019. The search terms used in PubMed
included (“glaucoma”[MeSH Terms] OR “glaucoma”[All Fields] OR
“glaucomatous”[All Fields] OR “glaucomatous”[All Fields]) AND (“machine
learning”[All Fields] OR “deep learning”[All Fields] OR “Computer Aided”[All])
AND (“diagnosis”[All Fields] OR “detection”[All Fields] OR “Screening”[All
fields]). The search terms in Embase included (‘glaucoma’/exp OR glaucoma) AND
(‘machine learning’ OR ‘deep learning’ OR ‘computer aided diagnosis’) AND
(‘diagnosis’ OR ‘detection’ OR ‘screening’).
The retrieved studies were imported into RevMan 5
(version 5.3. Copenhagen: The Nordic Cochrane Center, the Cochrane
Collaboration, 2014). All duplicates were deleted. The titles and abstracts of
the remaining articles were reviewed by two authors (Murtagh P and Greene G)
and those that did not meet the inclusion criteria were removed. For
completion, the reference lists from the selected studies were also examined.
Inclusion and Exclusion Criteria Machine learning in diagnostic imaging is a field which
is still in its infancy and so therefore there were a limited number of robust
papers on the subject. Inclusion criteria consisted of all observational
studies examining machine learning in the diagnosis and/or screening of
glaucoma involving fundal photographs and OCT imaging. Exclusion criteria
included studies which used human interpretation of fundal images, those whose
machine learning was based on perimetry, those only associated with diabetic
macular oedema or ARMD, participants under 18 years old and those with
neurological or other disorders which may confound visual field results. Some
of these studies did not define their diagnostic criteria but stated that the
diagnosis was of glaucoma was made by an ophthalmologist.
Data Extraction
For each study we recorded the
name of the principal author, year of publication, the number of eyes involved
in the study (both glaucomatous and healthy), the machine learning classifier
used (if multiple were utilised, the classifier with the most favourable result
was taken), how the classifier was trained and tested, their definition of
glaucoma diagnosis, make of OCT scanner in the cohort that used OCT and their
results.
Measurements of Diagnostic Accuracy Results were recorded in the papers as either the AUROC
or in terms of sensitivity and specificity. A receiver operating characteristic
(ROC) curve is a statistical representation which demonstrates the diagnostic
ability of a binary classifier at varying discrimination thresholds[34]. A ROC curve is generated by plotting true positive
rates against false positive rates or by (1-specificty) on the x-axis and
sensitivity on the y-axis. The AUROC informs us about the ability of the model
to distinguish between different classes. It is the outcome measure most used
to assess the reliability of a machine learning diagnosis. The results range
from 0.5-1, the closer the result is to one, the better the performance of the
machine leaning model[35]. Sensitivity is the
proportion of true positives that are correctly identified by the test.
Specificity is the proportion of true negatives that are correctly identified
by the test[36]. Although these two outcome
parameters are not directly comparable, we used an algorithm to calculate an
average of the sensitivity and specificity values in terms of AUROC in the
papers that failed to define one. ROC curves illustrate sensitivity and specificity
at different cut-off values. If only sensitivity and specificity are stated in
the studies, then there must be a single cut-off value being used, but this is
not always stated (and may not be known, since it is sometimes a hidden
parameter of the machine learning model).
Assessment of Study Quality All studies available were observational studies and
therefore there was no defined standard evaluation of bias. We consequently
established an adapted scoring system based on the Newcastle-Ottawa Scale (NOS)[37]. Each study was assessed on the following criteria:
1) sample size (greater than 100, 1 point; less than 100, 0 points); 2)
validation technique (cross validation, 1 point; other, 0 points); 3) unique
database (unique database, 1 point; previously utilised database, 0 points); 4)
their definition of glaucoma and diagnostic criteria (Anderson Patella
criteria, 1 point; other, 0 points); 5) inclusion of a confidence intervals
(CIs) around reported outcomes (yes, 1 point; no, 0 points), and 6) their
interpretation and reporting of results (AUROC, 1 point; other, 0 points).
Studies which scored four points or greater were deemed to be of a higher
methodological standard.
Statistical Analysis
Results of studies employing
fundal images and OCT were extracted using Cochrane RevMan software (version
5.3. Copenhagen: The Nordic Cochrane Center, the Cochrane Collaboration, 2014)
and Meta-analysis was performed to compare the accuracy of diagnosis. In the
majority of studies, diagnostic accuracy was summarised by the AUROC. Summary
estimates of the combined AUROC for each imaging methodology were estimated by
inverse-variance weighted Meta-analysis following the method of Zhou et al[38].
However, several studies employing fundal images reported
only a single sensitivity and specificity point. A single summary AUROC value
for these studies was derived by estimating a Heirarchical Summary ROC curve
(HSROC)[39]. The HSROC was estimated by
hierarchical logistic regression using the Metandi and Midas[40]
packages in Stata version 15.0 (StataCorp, College Station, TX, USA). This
summary AUROC value and associated standard error (SE) were included in the
Meta-analysis of fundal image studies.
Comparison of accuracy of fundal image and OCT studies
was performed by comparing pooled estimates and SE obtained through
Meta-analysis of each cohort. Significant difference was defined by a P
value less than 0.05.
Potential Confounders Potential Confounders include the same data set being
used by different studies, training and testing on the same data set,
discrepancy in glaucoma diagnosis, concomitant neurological disorders which may
confound results, use of crowdsourcing platforms and focus on computer methods
as opposed to clinical outcomes.
RESULTS
Selection Process and Search Results Our search parameters returned a total of 131 papers from
PubMed and 154 from Embase giving us a total of 285 studies. The titles and
abstracts of these studies were reviewed, duplicates were removed, alongside
the papers that did not fulfil the inclusion or exclusion criteria, and 36
papers were deemed suitable for revision in full text. Following comprehensive
appraisal, a total of 23 papers were deemed suitable for inclusion in this
Meta-analysis. This consisted of 13 papers which examined machine learning in
the diagnosis of glaucoma using fundal photos and 10 using OCT technology. All
studies were population based observational studies. Figure 1 outlines the
selection process.
Figure 1 Flow diagram depicting the selection process for
inclusion in the Meta-analysis.
Table 1 tabulates the data with regards machine learning
and fundal images. Ten of the thirteen studies were from Asia, nine of which
were from India[41-49] and one
from South Korea[50]. Of the remaining three
studies, one utilised a dataset from Germany[51],
one used fundal photos from two previous American studies[52]
(the African Descent and Glaucoma Evaluation Study and the Diagnostic
Innovations in Glaucoma Study) and the comprehensive study by Li et al[53] used the large online dataset Labelme (a
crowdsourcing platform for labelling fundal photographs). Of the studies
undertaken in India, six utilised the Kasturba Medical College dataset[42-44,46,48-49] and two used the Venu Eye Research Centre dataset[45,47] but using different machine
learning algorithms. The studies were published between 2009 and 2018. A total
of 59 788 eyes were included in the studies, 39 745 coming from a single study[53].
Table
|
Paper |
Classifier |
Number,
age |
Training
and testing |
Results |
Glaucoma
diagnosis |
Database |
|
Nayak et al 2009[42] |
ANN |
61, |
46 images
used for training, 15 images used for testing |
AUROC
0.984 (sensitivity 100%, specificity 80%), no CI |
Ill-defined
but by an ophthalmologist |
Kasturba
Medical College, Manipal, India |
|
Bock et al 2010[51] |
SVM |
575, |
5 fold
cross validation |
AUROC
0.88 P<0.07, sensitivity 73%, specificity 85% |
Ill
defined, stated gold standard |
Erlangen
Glaucoma Registry, Germany |
|
Acharya et al 2011[43] |
SVM |
60, |
5 fold
cross validation |
91%
accuracy, no CI, stated P significant is <0.05 |
Ill
defined |
Kasturba
Medical College, Manipal, India |
|
Mookiah et al 2012[44] |
SVM |
60, |
3 fold
stratified cross validation |
Accuracy
93.33%, sensitivity 86.67%, specificity 93.33%, AUROC 0.984, no CI, stated P significant is <0.05 |
Ill-defined
but by an ophthalmologist |
Kasturba
Medical College, Manipal, India |
|
Chakrabarty et al 2016[41] |
CNN |
314, |
1926 to
train, 314 to test |
AUROC
0.792 |
Gold
standard. Diagnosed by 4 glaucoma specialist |
Aravind
Eye Hospital, Madurai
and Coimbatore, India |
|
Issac et al 2015[45] |
SVM |
67, |
Leave one
out cross validation |
Accuracy
94.11%, sensitivity 100%, specificity 90%, no CI, P significant if less than 0.05 |
Ill-defined
but by an ophthalmologist |
Venu Eye
Research Centre, New Delhi, India |
|
Maheshwari et al 2017[46] |
SVM |
Two
databases, 60, |
Three fold
and tenfold cross validation |
Accuracy
98.33%, sensitivity 100%, specificity 96.67%, no CI, P significant if less than 0.05 |
Ill-defined
but by an ophthalmologist |
Medical
Images analysis Group Kasturba
Medical College, Manipal, India |
|
Singh et al 2016[47] |
SVM |
63, |
Leave one
out cross validation, 44 to train 19 to check |
Accuracy
95.24%, sensitivity 96.97%, specificity 93.33%, no CI, P significant
if less than 0.05 |
Ill-defined
but by an ophthalmologist |
Venu Eye
Research Centre, New Delhi, India |
|
Maheshwari et al 2017[48] |
LS-SVM |
488, |
Three fold
and tenfold, cross validation |
Accuracy
94.79%, sensitivity 93.62%, specificity 95.88% |
Ill-defined
but by an ophthalmologist |
Kasturba
Medical College, Manipal, India |
|
Raghavendra et al 2018[49] |
SVM |
1426, 589 N |
70%
raining, 30% testing, repeated 50 times, random training and testing
partitions |
Accuracy
98.13%, sensitivity 98%, specificity 98.3%, no CI, P significant if less than 0.05 |
Ill-defined
but by an ophthalmologist |
Kasturba
Medical College, Manipal, India |
|
Ahn et al 2018[63] |
CNN |
1542, |
Randomly
partitioned into 754 training, 324 validation and 464 test datasets |
AUROC
0.94, accuracy 87.9%, no CI |
Ill-defined
but likely Anderson Patella Criteria |
Kim’s Eye
Hospital, Seoul, South Korea |
|
Christopher et al 2018[52] |
CNN |
14822, |
10 fold
cross validation |
AUROC
0.91 (0.9-0.91 CI) |
Independent
masked graders |
The ADAGES
study, New and Alabama DIGS
Study, California |
|
Li et al 2018[53] |
CNN |
39745, |
8000
images as the validation set, and 31745 images as training set |
AUROC
0.986 (95%CI,
0.984-0.988) |
Grading
by trained ophthalmologists |
Label
me Data Set |
G: Glaucoma; N: Normal; AUROC: Area under the receiver
operating characteristics curve; CI: Confidence interval; CNN: Convolutional
neural networks; ANN: Artificial neural network; SVM: Support vector machine;
LS-SVM: Least squares support vectors machine; ADAGES: African descent and
glaucoma evaluation study; DIGS: Diagnostic innovations in glaucoma study.
Table 2 illustrates the data with regards machine
learning and OCT imaging techniques. There is a total of ten studies published
between 2005 and 2019. Five of the studies are from the USA[54-58], two are from Japan[50,59], two are from Brazil[60-61] and the remaining study is from Sweden[62]. There was no overlap between the between the studies
as regards data sets. Three studies used the Stratus OCT[54-55,62], three studies used the Cirrus
OCT (one standard definition[60] and two high
definition[56,61]), Topcon OCT
was used in two[50,57] and the
RS 3000[50] and Spectralis[58]
was used in one study each. The studies were published between 2005 and 2019. A
total of 1743 eyes were included in the OCT studies.
Table
|
Paper |
Classifier |
Number |
Training
and testing |
Results |
OCT |
Glaucoma
diagnosis |
Database |
|
Burgansky-Eliash et al 2005[54] |
Multiple-take
SVM |
89, |
Six fold
validation, leave one out |
AUROC
0.981, no CI |
Stratus
OCT |
Anderson
Patella Criteria |
Recruitment
of Subjects, Pennsylvania |
|
Bowd et al 2008[55] |
RVM |
225, |
Tenfold
cross validation |
AUROC
0.809, no CI |
Stratus
OCT |
Anderson
Patella Criteria |
Observational
Cross Sectional Study, California |
|
Bizios et al 2010[62] |
SVM |
152, |
Tenfold
cross validation |
AUROC
0.977, CI 0.959-0.999 |
Stratus
OCT |
Anderson
Patella Criteria |
Observational
Cross Sectional Study, Citizens of Malmo Sweden |
|
Barella et al 2013[60] |
RAN |
103, |
Tenfold
cross validation resampling |
AUROC
0.877, CI 0.810-0.944 |
Cirrus
SD OCT |
Anderson
Patella Criteria |
Glaucoma
Service UNICAMP, Brazil, prospective, observational cross sectional |
|
Silva et al 2013[61] |
RAN |
110, |
Tenfold
cross validation |
AUROC
0.807, CI 0.721-0.876 |
Cirrus
HD OCT |
Anderson
Patella Criteria |
Glaucoma
Service UNICAMP, Brazil, observational cross sectional |
|
Xu et al 2013[56] |
Boosted
logistic regression |
192, |
Normative
database, Tenfold cross validation |
AUROC
0.903, no CI |
Cirrus
HD OCT |
Anderson
Patella Criteria |
PITT
trial, Pennsylvania |
|
Muhammad et al 2017[57] |
CNN |
102, |
Pretrained,
leave one out cross validation |
AUROC
0.945, CI 0.955-0.947 |
Topcon
OCT |
Anderson
Patella Criteria |
From
previous study for OCT and early glaucoma diagnosis, New York |
|
Asaoka et al 2019[59] |
SVM |
178, |
Pre
training, glaucoma OCT database |
AUROC
0.937, CI 0.906-0.968 |
RS 3000 |
Anderson
Patella Criteria |
Japanese
Archives of Multicentral Images of Glaucomatous OCT database, Japan |
|
Christopher et al 2018[58] |
PCA |
235, |
Leave
one out approach |
AUROC
0.95, CI 0.92-0.98 |
Spectralis
OCT |
Ill
defined |
DIGS
dataset, California |
|
An et al 2019[50] |
CNN |
357, |
Tenfold
cross validation |
AUROC
0.963, Mean±SD 0.029 |
Topcon
OCT |
Anderson
Patella Criteria |
Observational
Cross Sectional Study, Japan |
G: Glaucoma; N: Normal; AUROC: Area under the receiver
operating characteristics curve; CI: Confidence interval; CNN: Convolutional
neural networks; ANN: Artificial neural network; SVM: Support vector machine;
LS-SVM: Least squares support vectors machine; ADAGES: African descent and
glaucoma evaluation study; DIGS: Diagnostic innovations in glaucoma study; PCA:
Principal component analysis; RVM: Relevance vector machine.
Assessment of Study Quality An assessment of study quality can be seen in Tables 3
and 4 with regards to the fundal photo and OCT groups respectively. We defined
a superior methodology as a score of four or greater. It can be observed that
the OCT group have a superior methodological standard than the fundal photo
group. All of the OCT group have a score of four point of greater, while only 5
of the 13 (38.46%) studies in the fundal photo group achieved this score.
Table 3 Assessment of study quality using modified NOS
with respect to fundal images
|
Paper |
Sample
size |
Validation
technique |
Unique
database |
Definition
of glaucoma |
CI |
AUROC |
Total |
|
Nayak et al 2009[42] |
|
|
|
|
|
X |
1 |
|
Bock et al 2010[51] |
X |
X |
X |
X |
|
X |
5 |
|
Acharya et al 2011[43] |
|
X |
|
|
|
|
1 |
|
Mookiah et al 2012[44] |
|
X |
|
|
|
X |
2 |
|
Chakrabarty et al 2016[41] |
X |
|
X |
X |
|
X |
4 |
|
Issac et al 2015[45] |
|
X |
|
|
|
|
1 |
|
Maheshwari et al 2017[46] |
X |
X |
|
|
|
|
2 |
|
Singh et al 2016[47] |
|
X |
|
|
|
|
1 |
|
Maheshwari et al 2017[48] |
X |
X |
|
|
|
|
2 |
|
Raghavendra et al 2018[49] |
X |
|
|
|
|
|
1 |
|
Ahn et al 2018[63] |
X |
|
X |
X |
|
X |
4 |
|
Christopher et al 2018[52] |
X |
X |
X |
|
X |
X |
5 |
|
Li et al 2018[53] |
X |
|
X |
|
X |
X |
4 |
CI: Confidence interval; AUROC: Area under the receiver
operating characteristics curve; NOS: Newcastle-Ottawa Scale; OCT: Ocular
coherence tomography.
Table 4 Assessment of study quality using modified NOS
with respect to OCT scans
|
Paper |
Sample
size |
Validation
technique |
Unique
database |
Definition
of glaucoma |
CI |
AUROC |
Total |
|
Burgansky-Eliash et al 2005[54] |
|
X |
X |
X |
|
X |
4 |
|
Bowd et al 2008[55] |
X |
X |
X |
X |
|
X |
5 |
|
Bizios et al 2010[62] |
X |
X |
X |
X |
X |
X |
6 |
|
Barella et al 2013[60] |
X |
X |
|
X |
X |
X |
4 |
|
Silva et al 2013[61] |
X |
X |
|
X |
X |
X |
5 |
|
Xu et al 2013[56] |
X |
X |
X |
X |
|
X |
5 |
|
Muhammad et al 2017[57] |
X |
X |
X |
X |
X |
X |
6 |
|
Asaoka et al 2019[59] |
X |
|
X |
X |
X |
X |
5 |
|
Christopher et al 2018[58] |
X |
X |
X |
|
|
X |
4 |
|
An et al 2019[50] |
X |
X |
X |
X |
X |
X |
6 |
CI: Confidence interval, AUROC: Area under the receiver
operating characteristics curve; NOS: Newcastle-Ottawa Scale; OCT: ocular
coherence tomography.
Definition of Glaucoma Definition of glaucoma diagnosis varied between the
studies. In the fundal photo study cohort, the majority[42-49] were ill defined but stated that they were diagnosed
by an ophthalmologist, three[41,51,63] stated that the diagnosis was gold standard and was
likely the Anderson Patella Criteria and the remaining 2 studies[52-53] were diagnosed using trained
independent masked graders. In the OCT studies group, nine of the ten studies[50,54-57,59-62] had their glaucoma diagnosis defined by the Anderson
Patella criteria. In the remaining paper[58], the
diagnosis was ill-defined but stated to be by two independent masked graders.
Machine Learning Classifier As regards the machine learning classifier, SVM[43-47,49-51]
was used in seven of the fundal photo group. Neural networks were used in five[41-42,52-53,63] and LS-SVM[48] was used
in one each. In the OCT cohort, the classifier used was more varied. Three
studies utilised SVM[54,59,62]. RAN[60-61]
and CNN[50,57] was utilised by
two studies each. Principal Component Analysis[58]
and Relevance Vector Machine[55] were used in one
study and the final study[56] employed boosted
logistic regression.
Validation Training and testing protocols are outlined in Tables 1
and 2. Cross validation was the most common method with tenfold, fivefold,
threefold and leave one out cross validation accounting for the practices in
nine[46,48,50,52,55-56,60-62], two[43,51],
one[44] and five[45,47,54,57-58]
of the studies respectively. A random partitioning of training and testing
occurred in four of the studies[49,53,59,63].
Meta-Analysis
Since AUROC was the most widely
reported and informative measure of diagnostic precision employed in the
included studies, Meta-analysis was performed based on pooling AUROC estimates
for each cohort.
Nine of the fundal image studies did not report an AUROC,
but gave only a single value of sensitivity and specificity[42-49,51]. In order to include these
studies in the larger Meta-analysis, we obtained a single pooled AUROC value by
estimating a HSROC. This is shown in Figure 2. AUROC curve was calculated to be
0.979; 95%CI: 0.887-0.996. Studies which only reported an AUROC but did not
include an estimate of variance or uncertainty (i.e., SE or CI) could
not be included in the Meta-analysis.
Figure 2 An HSROC estimated by pooling results of nine
fundal photo studies who did not report a AUROC value (sensitivity and
specificity only) Area under the summary ROC curve: 0.979;
95%CI: 0.887-0.996.
Tables 5 and 6 outline the results of the Meta-analysis
in terms of the fundal photo cohort (with the HSROC addition) and the OCT
cohort respectively.
Table 5 Meta-analysis, AUROC and estimated HSROC of studies
relating to fundal photos
|
Study |
AUROC |
SE |
95%CI |
z |
P |
Weight
(%) |
|
|
Fixed |
Random |
||||||
|
Christopher et al 2018[52] |
0.910 |
0.00200 |
0.906
to 0.914 |
|
|
19.92 |
34.92 |
|
Li et al 2018[53] |
0.986 |
0.00100 |
0.984
to 0.988 |
|
|
79.67 |
35.01 |
|
Others (HSROC estimate) |
0.979 |
0.0140 |
0.952
to 1.000 |
|
|
0.41 |
30.08 |
|
Total (random effects) |
0.957 |
0.0204 |
0.917
to 0.997 |
46.9 |
<0.001 |
100.00 |
100.00 |
AUROC: Area under the receiver operating characteristic
curve; CI: Confidence interval; HSROC: Hierarchical summary receiver operating
characteristic curve; SE: Standard error.
Table 6 Meta-analysis and AUROC of studies relating to
OCT studies
|
Study |
AUROC |
SE |
95%CI |
z |
P |
Weight
(%) |
|
|
Fixed |
Random |
||||||
|
Burgansky-Eliash et al 2005[54] |
0.981 |
0.0330 |
0.916
to 1.000 |
|
|
2.12 |
8.69 |
|
Bowd at al 2008[55] |
0.817 |
0.0300 |
0.758
to 0.876 |
|
|
2.56 |
9.19 |
|
Bizios et al 2010[62] |
0.977 |
0.0102 |
0.957
to 0.997 |
|
|
22.16 |
12.12 |
|
Barella et al 2013[60] |
0.877 |
0.0342 |
0.810
to 0.944 |
|
|
1.97 |
8.49 |
|
Silva et al 2013[61] |
0.807 |
0.0395 |
0.730
to 0.884 |
|
|
1.48 |
7.64 |
|
Xu et al 2013[56] |
0.903 |
0.0472 |
0.810
to 0.996 |
|
|
1.04 |
6.54 |
|
Muhammad et al 2017[57] |
0.945 |
0.0102 |
0.925
to 0.965 |
|
|
22.16 |
12.12 |
|
Asaoka et al 2019[59] |
0.937 |
0.0158 |
0.906
to 0.968 |
|
|
9.22 |
11.45 |
|
Christopher et al 2018[58] |
0.950 |
0.0153 |
0.920
to 0.980 |
|
|
9.85 |
11.52 |
|
An et al 2019[50] |
0.963 |
0.00917 |
0.945
to 0.981 |
|
|
27.44 |
12.22 |
|
Total (random effects) |
0.923 |
0.0174 |
0.889
to 0.957 |
53.1 |
<0.001 |
100.00 |
100.00 |
OCT: Ocular coherence tomography; AUROC: Area under the
receiver operating characteristic curve; CI: Confidence interval; SE: Standard
error.
It can be seen that there is no statistically significant
difference with respect to machine learning between fundal photos and OCT
images in diagnosing or screening for glaucoma. The total AUROC, in terms of
random effects, for the fundal photo cohort was calculated to be 0.957 (CI:
0.917 to 0.997, P<0.001) and 0.923 (CI: 0.889 to 0.957, P<0.001)
for the OCT cohort.
Figures 3 and 4 are Forest plots depicting a graphical
representation of weight and AUROC of both the fundal image cohort and the OCT
cohort respectively.
Figure 3 Forest plot of the AUROC of the fundal images
cohort.
Figure 4 Forest plot of the AUROC of the OCT cohort.
Funnel Plots for risk of bias were also performed for the
two cohorts and are outlined in Figures 5 and 6. The fundal image group only
has three points and therefore it has an ill-fitting funnel plot.
Figure 5 Funnel plot for fundal image studies.
Figure 6 Funnel plot for OCT studies.
The OCT funnel has every study include and is a greater
assessment of study bias in comparison to the fundal image plot. Due to the
fact that, unlike with standard diagnostic tests, diagnostic accuracy is
expected to increase with sample size in machine learning studies, one would
expect funnel plots in machine learning Meta-analysis to be asymmetric, with
the majority of studies falling in the lower left quadrant. A large number of
studies falling to the bottom-right would be suggestive of publication bias or
perhaps overfitting of machine learning models.
Tests for heterogeneity were also performed and these are
outlined in Table 7. The I2 value for the fundal image cohort
and the OCT cohort is 99.83% and 81.66% respectively which is indicative of a
high level of heterogeneity.
Table 7 Test for heterogeneity for fundal image studies
and OCT studies
|
Parameters |
Q |
Significance
level |
I2 (inconsistency) |
95%CI
for I2 |
|
Fundal image studies |
1155.5417 |
P<0.0001 |
99.83% |
99.77
to 99.87 |
|
OCT studies |
49.0860 |
P<0.0001 |
81.66% |
67.42
to 89.68 |
A comparison of sample size in terms of numbers used for
validation versus diagnostic accuracy (given as AUROC) was performed to examine
if any correlation existed.
Table 8 outlines the result of a Meta-analysis of the
fundal image group without the Li et al's[53]
study. The study was excluded due to the very high numbers, and therefore
effect it may have on the outcome of the analysis. It can be seen that the
total AUROC in terms of random effects has decreased from 0.957 to 0.942, a
difference of 0.015.
Table 8 Meta-analysis, AUROC and estimated HSROC of
studies relating to fundal photos with the exclusion of the Li study
|
Study |
ROC
area |
SE |
95%CI |
z |
P |
Weight
(%) |
|
|
Fixed |
Random |
||||||
|
Christopher et al 2018[52] |
0.910 |
0.00200 |
0.906
to 0.914 |
|
|
98.00 |
54.06 |
|
Others (SROC estimate) |
0.979 |
0.0140 |
0.952
to 1.000 |
|
|
2.00 |
45.94 |
|
Total (random effects) |
0.942 |
0.0242 |
0.894
to 0.989 |
38.858 |
<0.001 |
100.0 |
100.00 |
AUROC: Area under the receiver operating characteristic curve;
CI: Confidence interval; HSROC: Hierarchical summary receiver operating
characteristic curve; SE: Standard error.
DISCUSSION
Meta-Analysis
The findings of this
Meta-analysis have indicated that there is no statistically significant
difference with respect to machine learning between fundal photos and OCT
images in diagnosing or screening for glaucoma.
The total AUROC, in terms of random effects, for the
fundal photo cohort was calculated to be 0.957 (95%CI: 0.917 to 0.997, P<0.001)
and 0.923 (95%CI: 0.889 to 0.957, P<0.001) for the OCT cohort.
Although there is a difference of 0.034 between the two results, the CIs of
both groups overlap and there is no significant difference in diagnostic
accuracy between the two cohorts (P=0.34; t-test based on pooled
AUROC values and SE).
Sample Size There is a notable discrepancy between the sample sizes
of the OCT group (n=1743) and the fundal images group (n=59 788).
Although the number of studies is approximately on par (10 studies for OCT and
13 for fundal photos), there is over a 30-fold increase in the numbers of eyes
participating in the fundal photo group in comparison to the OCT group.
However, the majority (39 745) of these eyes come from a single study[53]. If we remove this study from our Meta-analysis, as
in seen in Table 8, the AUROC in terms of random effects is 0.942, leaving a
difference of just 0.019 between the two groups. It is known that machine
learning and deep learning are techniques that benefit from large databases for
training hence the bigger the training database, the more accurate the model[64]. This can be illustrated with our data. Figure 7
depicts a scatter plot of validation numbers versus AUROC. A linear trend line
shows that for increasing validation numbers there is an associated rise in
diagnostic accuracy. A similar trend can be seen in the funnel plot with
regards to the OCT cohort. There are multiple study sizes with low sample size
and low accuracy. Accuracy improves as the sample size gets bigger (smaller SE
and hence higher accuracy).
Figure 7 Graph of number used for validation versus
AUROC.
One reason for the variance in numbers is the ease of
acquisition of fundal photos in comparison to OCT scans. Large data sets such
as the “Labelme” dataset (http://www.labelme.org/assessed on
Data Sets There is substantial overlap between the datasets used in
the fundal imaging cohort. Six studies in this group used the Kasturba Medical
College dataset[42-44,46,48-49] and two used the Venu Eye
Research Centre dataset[45,47].
They used different machine learning models, however, three of the studies[43-44,48] had the
same number of participants, both glaucomatous and healthy, and it is possible
that they used exactly the same fundal photos as it does not state how many
images the dataset contains. This does not skew the findings but potentially
hampers the power of these studies. It is also seen that there is a heavy Asian
majority with regards number of papers in the fundal photo cohort. Ten of the
thirteen papers came from Asia, nine of them from India. There is an obvious
population based bias in this group and the same machine learning technique may
not be comparable to glaucoma diagnosis in a different ethnic cohort[66]. There is a greater ethnic diversity observed in the
OCT study groups. These are mainly population based studies and by their design
should limit the effect of selection bias.
Validation As stated in the results section above, cross validation
was the most utilised method of training and testing with some form of it being
used in seventeen out of the twenty-three studies. It has been stated that
cross validation is a better method for testing and training than random
allocation which occurred in four of the studies[29].
This is due to the fact that when random sampling is used, there is a chance
that the sampling set does not contain the disease or features associated with
the disease process. Four of our studies[49,53,59,63] used
random sampling and although initially may appear as the better teaching
process, the more robust technique of cross validation may make their models
more accurate.
Machine Classifier
The studies used a range of
different classifiers or machine learning algorithms. The most commonly used
algorithm was SVM being utilised in 10 of the studies. This is useful in
classifying linear features and as such is the option of choice in classifying
fundal images[67]. Problems arise with this
classifier when non-linear features are extracted such as are employed in OCT
scanning techniques. Hence more convoluted classifiers may be more appropriate
when interpreting these scans, e.g., CNN and ANN, and this is reflected
in the data.
The algorithms used in these studies were solely
constructed to aid in the diagnosis of glaucoma. This is usually a binary
classification as outlined with the abundant use of the SVM classifier.
However, in the clinical setting many patients can suffer from a multitude of
eye pathologies. Cataract, for instance, is an extremely common finding
especially in the elderly. Significant cataract can hamper the acquisition of
fundal photos and OCT images. It can also increase the amount of “noise” in the
attained scans making it more difficult for the algorithm to interpret it[64]. Patients may also have features of DR and/or ARMD,
pathologies which are very common in the aging population. A deep learning
algorithm was previously developed[68] and tested
on a small cohort (n=60) which aimed to detect a range of retinal
disease from fundal images. Accuracy dropped from 87.4% in the cohort which had
DR alone to 30.5% when multiple aetiologies were included. However, the small
dataset used for 10 identifiable diseases is likely to inflict bias on the
results.
Glaucoma Diagnosis
Glaucoma diagnosis is a
multifactorial process. It is a significant proportion of the workload of
general ophthalmologists. In order to make a definitive diagnosis perimetry,
fundoscopy, gonioscopy and tonometry must all be undertaken. There is
significant variance in the agreed diagnosis of glaucoma in the above studies.
Many have the diagnostic criteria ill-defined but state that is was established
by an ophthalmologist or multiple masked graders. The reason for this
inconsistency is that a significant proportion of the reviewed papers were
published in journals with an interest in computer methods as opposed to
clinical and ophthalmological findings. Their definitions are vaguer than those
published in the clinical journals. The variation in robust diagnoses can also
be observed between the fundal images and OCT cohort with all but one[58] of the OCT group having their glaucoma diagnosis
defined by the Anderson Patella criteria.
Incorporation of other patient parameters into the model
process, e.g., age, smoking status, intraocular pressure, visual field
testing has been shown to increase diagnostic accuracy[67],
although the incorporation of perimetry is likely to prove to be a time and
resource heavy inclusion parameter.
Methodological Quality The methodological quality of the studies was assessed
using a modified NOS. It can be witnessed that the OCT studies group have a
greater standard of quality as opposed to the fundal image group. Only 5 of the
13 (38.46%) studies in the fundal image group received a methodological score
of 4 or greater on our modified scale. This could be a potential confounder
with respect to the results of the analysis but due to the fact the studies
with a low score amount to 3.82% (2285 of 59 788) of the total number of eyes
in the fundal photos group, its effect is unlikely to be statistically
significant in the pooled analysis.
Publication Bias
The funnel plots, outlined in
Figures 5 and 6, indicate that there is low risk of publication bias especially
in OCT group. A total of three points are on the fundal image funnel plot and
so therefore it is difficult to make an assumption about the bias of these
studies. It can be seen that a larger sample size (and as such a smaller SE)
will give a large degree of accuracy. Due to the fact that, unlike with
standard diagnostic tests, diagnostic accuracy is expected to increase with
sample size in machine learning studies, one would expect funnel plots in
machine learning Meta-analysis to be asymmetric, with the majority of studies
falling in the lower left quadrant. A large number of studies falling to the
bottom-right would be suggestive of publication bias or perhaps overfitting of
machine learning models.
Heterogeneity
There is a large degree of
heterogeneity as outlined in Table 7. The I2 value for the
fundal image cohort and the OCT cohort is 99.83% and 81.66% respectively. This
is not surprising given the different methods, sample sizes and algorithms used
in each.
Glaucoma Prevalence
The prevalence of primary open
angle glaucoma in the general population is approximately 2% over the age of 40
and increases with age to affect 4% of those over 80[69].
On review of our data sets, it is seen that that the proportional of
glaucomatous eyes in our cohorts ranged from 23.35%[53]
to 77.08%[56], with an average of 53.05%±11.66%
SD. This indicates that, during the training and testing process, the algorithm
is more likely to observe glaucomatous eye on average thirteen times more
frequently than it would in the general population. Validation on such datasets
may lead to an increase in false positives either by the algorithm “expecting”
to have more positive results than it has or secondary to overfitting of
potential disease characteristics.
Unsupervised Machine Learning Although our studies solely examined supervised machine
learning, unsupervised machine learning in the form of deep learning will
generate decisions based on high dimensional interpretation and neural networks
that humans cannot interpret and is likely to be the next step in evolution of
computer aided diagnosis. We fundamentally do not know how they make their
judgments[70]. Areas of the image can be
highlighted, but often they are not associated with the pathological process
from our understanding. This can aid us to look for new aetiologies for retinal
disease process. However, this is termed a “black box” as we don’t fully
understand how the algorithms are coming to their conclusions.
Challenges In a recent review by Ting et al[71], a number of potential challenges for AI
implementation into clinical practice were identified. The algorithm requires a
large number of pathological images to train. The sharing of images between
centres is currently an ethical grey area but to ensure adequate classification
by the algorithm, data must be shared between centres. This includes a range of
date from diverse populations. Rare ocular disease may also prove an issue, as
there may not be enough images to adequately train an algorithm to recognise
these diseases.
Limitations Limitations of our studies include the high prevalence of
articles in computer method journals as opposed to clinical journals especially
in the fundal photo group. They were more preoccupied with how the algorithm
and classifier functioned from a computer science point of view and their
definitions of glaucoma were not as robust as they were in the clinical
journals. Although our studies scored low on our assessment of quality, these
studies had to be included due to the paucity of papers on the subject.
The use of the same database and crowdsourcing material
has the potential to bias the results of the Meta-analysis. Ideally all studies
would have the same definition of glaucoma, use a separate training, validation
and testing set and use cross validation.
In conclusion, OCT scanning provides micrometre
resolution of the RNFL and one would assume that it should be related to a more
accurate screening and diagnostic tool. However, we have demonstrated that the
literature to date has failed to corroborate this with respect to machine
learning. The ease of access and lower cost associated with fundal photo
acquisition make that the more appealing option in terms of screening on a
global scale, however further studies need to be undertaken on both groups
owing largely to the poor study quality associated with the fundal photography
cohort.
The prospect of machine learning in the screening and diagnosing
of ocular disease is a very appealing prospect. It can take pressure off
ophthalmology departments and allow a greater throughput of the population to
enjoy a better vision related quality of life. However, care should be taken in
interpreting these findings. During an ocular assessment, ophthalmologists take
in a holistic view of the patient, including past medical history and
medications, and may undertake multimodal imaging e.g. angiograms and/or
perimetry to come to a complete diagnosis. We know that patients greatly value
their interaction with their doctor[72] and the
“human-touch” associated with it, something which could be become extinct with
the advent of AI. Another cause of concern is the “black box” nature of
decision making in unsupervised machine learning. With so much emphasis
nowadays on evidence-based medicine, it may not yet be time to place all blind
trust in machines.
ACKNOWLEDGEMENTS
Conflicts of Interest: Murtagh P, None; Greene G, None; O’Brien
C, None.
REFERENCES