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230
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K. G. Pratinya et al.
Table 19.1 Showing the evolution of the deep learningbased optic disc image analysis
Study Method
Sihota etal. [20] Moorelds regression
analysis
Almazrao etal. [21] Deep learning-based
segmentation algorithms
Park etal. [22] Deep learning-based vCDR
estimation
Lee etal. [23] Deep learning-based RNFL
analysis
19.6.1 AI Framework
There are two commonly used approaches in the
AI framework for optic disc analysis and glaucoma detection: the one-step and two-step
frameworks.
1. One-step framework: A single deep learning
model is trained to directly classify optic disc
images as normal or glaucomatous in the onestep framework. The model takes the raw
input images and performs both feature
extraction and classication in a single step.
This approach leverages the ability of deep
learning algorithms to automatically learn relevant features and make predictions from the
raw data.
2. Two-step framework: In the two-step framework, the optic disc analysis process is divided
into two distinct steps: feature extraction and
classication.
Step 1: Feature extraction:
• Deep learning models extract features
from the optic disc images, such as optic
disc cupping, rim thickness, vessel characteristics, or other relevant parameters associated with glaucoma.
• The feature extraction step aims to capture
informative representations of the optic
disc that can be used for subsequent
classication.
Step 2: Classication:
• The extracted features are fed into a sepa-
rate classication algorithm or model, such
as a support vector machine (SVM) or ran-
dom forest classier, to perform the nal
classication task.
• The classier uses the extracted features as
input and determines whether the optic
disc is normal or shows signs of
glaucoma.
While the one-step framework simplies the
workow by combining feature extraction and
classication into a single model, reducing complexity and computational requirements, the twostep framework allows extracting specic
features relevant to glaucoma detection. These
features can be further analyzed and interpreted
by clinicians, and it also provides exibility in
selecting different classiers for the classication
step; it thus enables the use of various algorithms
depending on the specic requirements and characteristics of the dataset. The superiority of the
two-step process over the one-step process in
optic disc analysis for glaucoma detection
depends on various factors and the specic context of the application [13].
Various studies were done to assess the agreement of AI with manual grading [14–19].
For example, Shroff et al. [14] studied the
agreement of vCDR measured by an ofine
AI-integrated smartphone fundus camera with
SD-OCT vCDR measurements and manual grading by experts on a stereoscopic fundus camera
on 473 eyes from 244 patients. They found that
the ofine AI had an excellent agreement and
correlation with the SD-OCT and manual grading. MacCormik et al. [18] studied a glaucoma
detection algorithm that segments and analyzes
color photographs to quantify optic nerve rim
consistency around the entire disc at 15-degree
intervals. They found that it accurately distinguished glaucomatous and healthy discs on internal and external validation with an area under the
ROC of 99.6% and 91.0%, respectively.
Deep learning algorithms can assist in glaucoma screening and diagnosis by analyzing
optic disc photographs. By analyzing the
enhanced images and comparing them with a
vast database of reference images, the system
can highlight potential abnormalities, suggest
follow-up tests, or provide risk stratication for
glaucoma [13].
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Future directions: Ongoing research and
development in AI and deep learning continue to
advance the eld of enhanced optic disc photography. Efforts are being made to incorporate multimodal imaging data, such as combining optic
disc photographs with OCT or visual eld data,
to improve diagnostic accuracy further.
Additionally, integrating AI algorithms into portable and handheld imaging devices opens up the
possibilities for point-of-care screening and
remote monitoring [14].
19.7 Conclusion
Optic disc photography has played a pivotal role
in the screening and diagnosing glaucoma, contributing to the early detection and management
of this sight-threatening disease. Technological
advancements have revolutionized optic disc
image analysis from its historical roots in the
early twentieth century to the modern era.
AI-powered frameworks have emerged as transformative tools for automated segmentation and
abnormality detection. These developments have
enabled large-scale, low-cost screening, and
extended eye care services to remote areas lacking access to qualied ophthalmologists. As
research in this eld continues, we anticipate further innovations that will enhance the accuracy
and accessibility of glaucoma screening and
management, offering a brighter outlook for
patients worldwide.
Funding Hyderabad Eye Research Foundation.
Disclosure Nil.
References
1. Panwar N, Huang P, Lee J, etal. Fundus photography
in the 21st century—a review of recent technological
advances and their implications for worldwide healthcare. Telemed J E Health. 2016;22(3):198–208.
2. Yannuzzi LA. The retinal atlas. Elsevier Health
Sciences; 2010. p.929.
3. Keeler R, Singh AD, Dua HS.Reecting on reections: Gullstrand’s large reex-free ophthalmoscope.
Br J Ophthalmol. 2010;94(7):826–6.
4. Myers J, Fudemberg S, Lee D. Evolution of optic
nerve photography for glaucoma screening: a
review: evolution of disc photos for screening. Clin
Experiment Ophthalmol. 2018;46(2):169–76.
5. Varma R, Steinmann WC, Scott IU. Expert agreement in evaluating the optic disc for glaucoma.
Ophthalmology. 1992;99(2):215–21.
6. Morgan JE, Sheen NJL, North RV, etal. Digital imaging of the optic nerve head: monoscopic and stereoscopic analysis. Br J Ophthalmol. 2005;89(7):879–84.
7. Parkin B, Shuttleworth G, Costen M, Davison
C. A comparison of stereoscopic and monoscopic
evaluation of optic disc topography using a digital optic disc stereo camera. Br J Ophthalmol.
2001;85(11):1347–51.
8. Yang J, Qu Y, Zhao J, et al. Stereoscopic vs. monoscopic photographs on optic disc evaluation and glaucoma diagnosis among general ophthalmologists: a
cloud-based real-world multicenter study. Front Med
(Lausanne). 2022;9:990611.
9. Varshney T, Parthasarathy DR, Gupta V. Articial
intelligence integrated smartphone fundus camera
for screening the glaucomatous optic disc. Indian J
Ophthalmol. 2021;69(12):3787–9.
10. Das S, Kuht HJ, De Silva I, et al. Feasibility and
clinical utility of handheld fundus cameras for retinal
imaging. Eye (Lond). 2023;37(2):274–9.
11. Bak E, Choi HJ. Structure-function relationship in
glaucoma: optical coherence tomography en face
imaging vs. red-free fundus photography. Eye (Lond).
2023;37(14):2969–76.
12. Watanabe T, Hiratsuka Y, Kita Y, et al. Combining
optical coherence tomography and fundus photography to improve glaucoma screening. Diagnostics
(Basel). 2022;12(5):1100.
13. Coan L, Williams B, Venkatesh KA, etal. Automatic
detection of glaucoma via fundus imaging and articial intelligence: a review. Surv Ophthalmol.
2023;68(1):17–41.
14. Shroff S, Rao DP, Savoy FM, etal. Agreement of a
novel articial intelligence software with optical
coherence tomography and manual grading of the optic
disc in glaucoma. J Glaucoma. 2023;32(4):280–6.
15. Youse S. Clinical applications of articial intelligence in glaucoma. J Ophthalmic Vis Res.
2023;18(1):97–112.
16. Mvoulana A, Kachouri R, Akil M.Fully automated
method for glaucoma screening using robust optic
nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images. Comput Med Imaging Graph.
2019;77:101643.
17. Bajwa MN, Malik MI, Siddiqui SA, et al. Twostage framework for optic disc localization and
glaucoma classication in retinal fundus images
using deep learning. BMC Med Inform Decis Mak.
2019;19(1):136.
18. MacCormick IJC, Williams BM, Zheng Y, et al.
Accurate, fast, data efcient and interpretable
glaucoma diagnosis with automated spatial analy-
232
https://t.me/med1917
K. G. Pratinya et al.
sis of the whole cup to disc prole. PLoS One.
2019;14(1):e0209409.
19. Zhao R, Chen X, Liu X, et al. Direct cup-to-disc
ratio estimation for glaucoma screening via semisupervised learning. IEEE J Biomed Health Inform.
2020;24(4):1104–13.
20. Sihota R, Gulati V, Agarwal HC, et al. Variables
affecting test-retest variability of Heidelberg retina
Tomograph II stereometric parameters. J Glaucoma.
2002;11(4):321–8.
21. Almazroa A, Burman R, Raahemifar K,
Lakshminarayanan V. Optic disc and optic cup seg-
mentation methodologies for glaucoma image detection: a survey. J Ophthalmol. 2015;2015:180972.
22. Park K, Kim J, Lee J. Automatic optic nerve head
localization and cup-to-disc ratio detection using
state-of-the-art deep-learning architectures. Sci Rep.
2020;10(1):5025.
23. Lee T, Jammal AA, Mariottoni EB, Medeiros
FA.Predicting glaucoma development with longitudinal deep learning predictions from fundus photographs. Am J Ophthalmol. 2021;225:86–94.
OCT inGlaucoma
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K.GowriPratinya , AparnaRao , PallaviRay,
andBhoomiThakkar
20
20.1 Introduction
Glaucoma is a chronic progressive optic neuropathy characterized by optic nerve head changes due
to the progressive loss of retinal ganglion cells.
Diagnosing this structural damage early is crucial
in managing glaucoma. Though it can be clinically
assessed by optic nerve head (ONH) and peripapillary retinal nerve ber layer (RNFL) examinations,
the introduction of optical coherence tomography
(OCT) has allowed for supplemental objective and
quantitative evaluation of ONH and RNFL changes.
OCT is a noninvasive, noncontact technique that
uses the differences in the optical properties of tissue structures to create an “optical biopsy” thereby
providing high- resolution and cross-sectional
imaging of the ONH, RNFL, and macula [1].
20.2 Technology
Optical coherence tomography is based on the
principle of the Michelson low-coherence interferometry. This imaging technique directs waves
K. G. Pratinya · A. Rao (*)
Kallam Anji Reddy Campus, L V Prasad Eye
Institute, Hyderabad, India
e-mail: kolipaka.pratinya@lvpei.org; aparna@lvpei.org
P. Ray · B. Thakkar
Mithu Tulsi Chanrai Campus, L V Prasad Eye
Institute, Bhubaneswar, India
e-mail: pallavi.ray@lvpei.org;
bhoomi.Thakkar@lvpei.org
to the tissue under examination, where the waves
echo off the tissue structure. The reected waves
are analyzed, and their delay is measured to document the depth at which the reection occurred.
OCT uses light in the near infrared. The delay of
the reected waves cannot be measured directly,
so a reference measurement is used. In an interferometer, part of the light is directed to the sample, and another portion is sent to a reference arm
with a well-known length. These techniques were
adapted into time domain (TD) OCT and evolved
into Fourier domain-OCTs.
20.2.1 Time-Domain OCT
The rst commercial TD-OCT was OCT 1 by
Carl Zeiss Meditec (Dublin, CA, USA) [2]. The
light of a low-coherence source is guided to the
interferometer. The input beam is split into the
sample and reference beams by a beam splitter.
The reference beam travels to a mirror on a translational stage and is reected back. The light
reected from the eye is compared with that
reected from the reference mirror using a lowcoherence interferometry system. The reference
mirror is moved mechanically from the beam
splitter to estimate the depth of the reection
within the tissue. Multiple axial scans are created
to construct B-scans. Final 2D or 3D images are
created by computer systems in the OCT devices
either in grayscale or false colors.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
T. Das, P. Satgunam (eds.), Ophthalmic Diagnostics, https://doi.org/10.1007/978-981-97-0138-4_20
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20.2.2 Spectral-Domain OCT
Spectral-domain (SD) OCT techniques use
Fourier domain transformation [3]. Fourier transformation helps transform the signals between
two different domains, from the time- or spatial
domain to the frequency domain [4]. Using the
Fourier transformation, the technology from the
original method of TD-OCT, the SD-OCT, was
developed. While the TD-OCT encoded the location of each reection in the time information
relating the position of a moving reference mirror
to the location of the reection, SD-OCT, instead,
acquires all data in a single axial scan through the
tissue simultaneously by evaluating the frequency
spectrum of the interference between the reected
light and a stationary reference mirror. Zeiss
Cirrus HD-OCT (Carl Zeiss Meditec, Dublin,
CA, USA), Heidelberg Engineering Spectralis
OCT (Heidelberg Engineering, Heidelberg,
Germany) Optovue RTVue (Optovue Inc.,
Fremont, CA, USA), and Topcon 3D OCT 2000
(Topcon Medical Systems, Oakland, NJ) use the
SD-OCT system.
Advantages of SD-OCT over TD-OCT:
• It uses a xed reference mirror, which acquires
18,000–70,000 A-scans/s compared to the
400A-scans/s TD-OCT acquires. Hence, SDOCT systems are 200–400 times faster than
TD-OCT systems.
• Increased scanning speed results in a higher
axial resolution (around 5μm).
• Due to increased scanning speed, the artifacts
caused by eye movements are reduced.
20.2.3 Swept-Source OCT
Swept-source OCT is based on the Fourier
domain principles. It combines the advantages of
TD-OCT and SD-OCT.The light source used in
SS-OCT is of a narrow bandwidth, which changes
and sweeps the wavelength across a narrow band
in time. The variation in frequency with time
encodes different echo delay times in the light
beams [5]. The light source in the SS-OCT is
divided into a spectrum using a swept-source
laser; therefore, unlike SD-OCT, a spectrometer
is not required. In SS-OCT, high-speed detectors
detect interference signals in time and measure
echo delays using Fourier transformation [6].
20.3 Clinical Utility ofOCT
inGlaucoma
The use of SD-OCT for glaucoma diagnosis is an
increasingly common practice. While progressive neuro-retinal rim changes such as thinning
and excavation are more specic for glaucoma,
the RNFL changes are more sensitive for detecting glaucoma. OCT plays a key role in detecting
early glaucoma not detected by the perimetric
examination.
20.3.1 Glaucoma Diagnosis
Standard automated perimetry is widely used for
the diagnosis, staging, and monitoring of glaucoma, but it is only likely to detect functional
decits after at least 20–40% of retinal ganglion
cells (RGCs) are lost [7, 8]. Additionally, visual
eld testing requires patient cooperation and
understanding and is often variable [9]. Picking
up the early glaucomatous structural damage,
such as thinning of the RNFL and inner retinal
layers, is fundamental for early diagnosis and
prevention of vision loss.
The diagnostic capabilities of SD-OCT
depend on the severity/stage of glaucoma; it performs better when discriminating healthy from
advanced disease rather than early stages of glaucoma [10].
Taking multiple parameters into account, such
as peripapillary RNFL, ONH, and macular
parameters, OCT is useful for diagnosing early
glaucoma (Fig.20.1a–c), staging the severity of
glaucoma, and evaluating risk in glaucoma suspects [11]. Although using multiple parameters
could increase false-positive results, structural
damage may be present in one parameter and not
the other, and thus it is helpful to have information from the macula, ONH, and RNFL in glaucoma diagnosis [10].
20 OCT inGlaucoma
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235
a
Fig. 20.1 (a) Red-free fundus photograph of the RE
showing a disc indicative of glaucoma with inferior nerve
ber layer defect. (b) Humphrey visual eld print out of
bc
20.3.2 Glaucoma Progression
To assess the progression of glaucoma with the
RNFL thickness obtained by OCT, we need to be
aware of a few factors: (a) thinning due to normal
aging; (b) the oor effect of this parameter; (c)
thinning suggestive of glaucoma progression,
both globally and within a sector; and (d) correlation of RNFL loss with visual eld loss.
The RNFL undergoes attrition with age in
healthy eyes at a mean rate of −0.48μm/year in
Cirrus and −0.60 μm/year in Spectralis images.
Glaucoma progression, on the other hand, has a
faster rate of RNFL thinning, ranging
from−0.98μm/year for the Cirrus to −2.12μm/
year for the Spectralis images. RNFL measurement continues to decrease as glaucoma advances,
but does not go to zero, a phenomenon known as
the “oor effect.” This is because the architectural
support of the Müller cells, astroglia, microglia,
and blood vessels do not allow the structure of the
RNFL to degenerate completely with the retinal
ganglion cell axons. Average RNFL oor values
range from 49.2μm for the Spectralis to 57μm
for the Cirrus to 64.7μm for the RTVue images.
Once the RNFL thickness reaches the oor, progression can still occur but cannot be detected by
OCT. Therefore, the clinician should consider
using macular OCT and the Humphry Visual
Field (HVF, Carl Zeiss, Dublin, CA, USA) to
monitor the progression of advanced glaucoma.
Each OCT device provides progression analysis, which can be event- or trend-based. The event-
the same eye showing a normal eld. (c) OCT (ONH and
RNFL analysis) showing inferior RNFL thinning on deviation and clock hour map (red arrow and red circle)
based analysis measures the difference between
baseline and follow-up measurements. A decrease
of >5 μm in average RNFL thickness indicates
glaucoma progression, while a decrease of 7–8μm
or more in a sector also suggests progression. The
trend-based analysis identies progression by
monitoring the slope of RNFL thickness over
time. Cirrus provides a glaucoma progression
algorithm based on events and trend, comparing
the RNFL thickness of individual pixels between
baseline and follow-up images. Pixels are coded
yellow if there is test–retest variability between a
follow-up and baseline image, while red indicates
the same change on three consecutive scans. In
Spectralis, the system looks for specic patterns in
retinal structures to position the retest scan in the
same location automatically; RNFL thickness
change and trend reports are plotted over time to
compare the rate of change. The RTVue system
offers a trend-based analysis, which includes sideby-side comparisons of global RNFL thicknesses,
six sectorial thickness analyses, and a regression
line to determine the slope and standard error.
Although progression analysis software can be a
great tool, especially in a busy clinic, one should
always review the original scans and the sectors, as
subtle changes in a small area can be easily missed.
Although SD-OCT has signicantly increased
signal-to-noise ratio and decreased motion artifacts than TD-OCT, both are prone to image artifacts. These artifacts include speckle noise,
segmentation errors (Fig. 20.2a, b), alignment
errors, low signal quality, software problems, and
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K. G. Pratinya et al.
Fig. 20.2 (a) Ganglion cell analysis showing false thinning over the macular region due to improper segmentation (red
arrow). (b) Repeat OCT of the same patient with proper segmentation (red arrow) and normal macular thickness
media opacities, such as reduced corneal clarity
or cataracts. Fixational eye movements, such as
ocular tremors, drift, and microsaccades, can further reduce image quality.
While SD-OCT devices still require a trained
imaging technician to minimize artifacts, it is
comparatively less operator dependent than
TD-OCT devices. Clinicians should be cautious
in interpreting these OCT ndings and avoid
overtreating people with false-positive results.
20.4 New Advances
inGlaucomaOCT
20.4.1 Adaptive Optics inOCT
As mentioned earlier, the diagnostic capacity of
OCT is dependent on the severity of glaucoma. The
OCT is more effective in discriminating healthy
from moderate and advanced diseases than early
glaucoma. In addition, wide anatomic variations of
the optic nerve head, several sources of error, and
the database having a limited number of subjects of
any single ethnicity limit the diagnostic performance of OCT in detecting glaucoma [12]. Adaptive
optics (AO) could ll this gap in retinal imaging for
the early diagnosis of glaucoma by compensating
for ocular aberrations. It has been combined with a
fundus camera, scanning laser ophthalmoscope,
and OCT to help with a better understanding of the
disease process (Fig.20.3). The OCT system has an
excellent axial resolution, and with the addition of
AO, its lateral resolution improves dramatically
[13]. The AO-OCT system has the best spot size (in
terms of axial and lateral resolution) among all the
other AO systems.
AO-OCT can be used in the following ways:
1. To quantify morphological changes in the
photoreceptor cells in the abnormal areas of
the cone-mosaic.
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237
2. To detect optic nerve head changes and abnormalities for monitoring glaucoma
progression.
3. Imaging subtle changes in the RNFL at the
preclinical stage and visualization of the individual RNFL bundles in vivo could help
detect and monitor glaucoma progression
early.
Adaptive optics is an evolving technology; it
is not without limitations. Its greatest limitation
is the narrow eld of imaging; only 0.225mm is
covered in a single image [14]. The technology is
adversely affected by media opacity, dry eye,
poor pupillary dilatation, and high refractive
error. The focal plane of reference needs adjustment each time to obtain images at a particular
depth. In addition, ocular motion artifacts can
affect the quality of images. The technology
demands expertise, and acquiring images can be
time-consuming.
20.4.2 Enhanced Depth Imaging
andArticial Intelligence
Application inOCT Glaucoma
Enhanced depth imaging (EDI) is used in OCT
imaging to enhance the visualization of the
deeper structures within the eye, particularly the
optic nerve head and the lamina cribrosa [15–18].
EDI-OCT imaging allows for a more detailed and
accurate assessment of the optic nerve head and
the lamina cribrosa, both critical structures in the
development and progression of glaucoma
(Fig.20.4). EDI-OCT imaging has proven to be a
valuable tool in the early detection and monitoring of glaucoma and is increasingly used in clinical practice.
20.4.2.1 Principle
The principle behind EDI-OCT imaging is similar to that of traditional OCT imaging. However,
EDI-OCT uses a specialized technique to enhance
Fig. 20.3 Adaptive optics (prototype) setup at L V Prasad eye institute, Hyderabad, India
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Fig. 20.4 An EDI Spectralis image through the optic disc showing segmentation of various retinal layers (original
photograph)
K. G. Pratinya et al.
the depth resolution of the image, allowing for
better visualization of deeper structures. In EDIOCT, the light source used to produce the image
is positioned closer to the eye, bringing the
deeper portion so the eye is closer to the zerodelay line of the highest sensitivity. This enables
greater penetration of the light into the tissues.
This results in a sharper and more detailed image
of the deeper structures, such as the optic nerve
head and the lamina cribrosa.
In contrast, high-penetration OCT is an OCT
imaging technique that uses longer wavelength
light to increase the depth of penetration of the
OCT signal. This technique allows for better
visualization of deeper structures in the eye, such
as the choroid and sclera.
Some of the commercially available OCT systems that offer EDI imaging are Spectralis OCT
(Heidelberg Engineering), Cirrus HD-OCT (Carl
Zeiss Meditec), Topcon 3D OCT-1 Maestro
(Topcon Medical Systems), RTVue XR Avanti
(Optovue), Triton Swept Source OCT (Topcon
Medical Systems), AngioVue OCT (Optovue),
DRI OCT Triton Plus (Topcon Medical Systems),
and Envisu R2210 OCT (Bioptigen, Inc.).
EDI-OCT imaging is a powerful tool for
studying the complex anatomical and physiological changes associated with glaucoma and is
increasingly used in clinical practice and
research.
20.4.2.2 Clinical Applicability ofEDI
Imaging onOCT
A. EDI-OCT imaging is very useful for studying
laminar bowing. In this phenomenon, the
lamina cribrosa (a thin, sieve-like structure
within the optic nerve head) bows inward or
outward in response to changes in intraocular
pressure. Laminar bowing is an important
factor in the development and progression of
glaucoma, and understanding this
phenomenon is critical for diagnosing and
managing the disease. EDI-OCT imaging
allows better visualization of the lamina
cribrosa, located deep within the eye and difcult to see using other imaging techniques
[15–17]. This can help to understand the
mechanisms underlying laminar bowing better and may lead to improved diagnostic and
treatment approaches for glaucoma.
B. EDI-OCT imaging is used to evaluate other
structural changes within the optic nerve
head and the surrounding tissues, such as
changes in the size and shape of the optic cup
and the presence of uid-lled spaces within
the retina.
C. EDI-OCT imaging can also be used to evalu-
ate the efcacy of glaucoma treatments by
monitoring changes in the optic nerve head,
lamina cribrosa, RNFL, and ganglion cell
layer (GCL) over time. The early detection of
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changes in these structures can enable clinicians to adjust treatment plans to manage the
disease better and improve patient outcomes.
The following laminar parameters can be
studied using EDI imaging on OCT in
glaucoma:
1. Lamina cribrosa depth is the distance
between the anterior surface of the lamina
cribrosa and the reference plane, usually the
Bruch’s membrane or the retinal pigment
epithelium. A deeper lamina cribrosa is associated with an increased risk of glaucoma
progression.
2. Lamina cribrosa thickness is the thickness of
the lamina cribrosa at its thinnest point. A
thinner lamina cribrosa is associated with an
increased risk of glaucoma progression.
3. Lamina cribrosa position is its position rela-
tive to the optic nerve head rim. A more posteriorly positioned lamina cribrosa is associated
with an increased risk of glaucoma
progression.
4. Lamina cribrosa curvature is the curvature of
the lamina cribrosa surface. A more curved
lamina cribrosa is associated with an increased
risk of glaucoma progression.
5. Pre-laminar tissue thickness is the thickness
of the tissue anterior to the lamina cribrosa,
including the retinal nerve ber layer and the
pre-laminar tissue. Thinning of the prelaminar tissue is associated with an increased
risk of glaucoma progression.
20.5 Articial Intelligence (AI)
Applications
inGlaucomaOCT
1. AI has several potential applications in OCT
imaging for glaucoma [16, 18–20]. AI algorithms can be trained to detect subtle changes
in the optic nerve head and surrounding tissues that may indicate glaucoma. This can
help clinicians identify patients at risk of
developing the disease and initiate appropriate monitoring and treatment.
AI in OCT imaging for glaucoma is useful
in monitoring disease progression. AI algorithms can analyze longitudinal datasets of
OCT images to identify changes in the optic
nerve head and surrounding tissues over time.
This can help clinicians detect subtle changes
that may indicate disease progression.
AI can also be used to assist in the management of glaucoma by predicting the efcacy
of different treatment options. By analyzing
datasets of OCT images from patients who
have undergone different treatment modalities, AI algorithms can predict the likelihood
of success for different treatments in individual patients.
2. New developments in AI-based applications
on OCT in glaucoma are listed below.
(a) Multimodal imaging: Combining differ-
ent types of imaging modalities, such as
OCT, fundus photography, and visual
elds, can provide a more comprehensive
view of the eye and improve the accuracy
of glaucoma diagnosis and monitoring.
(b) Personalized medicine: AI algorithms can
analyze large datasets of patient information and OCT images to identify individualized risk factors for glaucoma and
tailor treatment plans accordingly.
(c) Real-time monitoring: Real-time moni-
toring of RNFL parameters using AIbased algorithms can help to identify
changes in disease progression earlier and
adjust treatment plans accordingly.
(d) Big data analysis: AI-based algorithms
can analyze large datasets of OCT images
and patient data to identify trends and
patterns indicative of glaucoma. This can
help improve our understanding of the
disease and guide future research.
(e) Automated reporting: AI algorithms can
generate automated reports based on the
analysis of OCT images, reducing the
need for manual interpretation by
clinicians.
3. Several AI platforms exist for OCT analysis
of RNFL, optic disc, and macula for glaucoma. Some of the novelties in different AI
platforms are listed below.
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