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21 Visual Field
251
patient vary xation. The MRF tests include 10-2, 24-2, and 30-2, and interact with the patient during the screening process. The results of the MRF are saved to either cloud­based storage or can be integrated into the hospital’s electronic record system for easy storage and retrieval.
(d) Eyecatcher [28]: This VR-based platform
found good acceptance by 20 patients for home visual eld testing with good concor­dance between visual elds measured at home and in the clinic. The median test dura­tion was 4.5min, with the ambient illumina­tion in the patient’s surroundings not affecting visual eld measurements.
21.7.2 Laptop-Based Perimetry
Portable laptop perimeters are small and light­weight versions of traditional tabletop perime­ters [29, 30]. These are ideal for screening programs and remote locations with limited access to specialized equipment. Laptop-based perimeters typically use static stimuli, such as lights of different intensities and colors, to test the patient’s visual eld. They may also use dif­ferent testing strategies, such as threshold and suprathreshold perimetry, to evaluate the patient’s visual eld. Some examples of laptop­based perimeters used in glaucoma testing include the Medmont M700 Automated Perimeter, [29] the Oculus Easyeld Perimeter, [30] and the Octopus 900 Perimeter.
21.7.2.1 Clinical Applications
The advantages of laptop-based perimeters over traditional tabletop perimeters include greater exibility and portability, reduced cost, and the ability to test in various settings. The limitations are reduced testing accuracy, limited testing range, and the need for frequent calibration and maintenance. One study [31] found a reasonable ROC curve for a home-based visual eld exami­nation method using a PC monitor or virtual real­ity (VR) glasses using the suprathreshold algorithm for the central 24° and the HVFA.
21.7.2.2 Head-Mounted or VR
Technology
Head-Mounted or VR Technology is an emerging method for visual eld testing in glaucoma. In VR visual eld testing, patients wear a VR head­set that displays stimuli in a 360° virtual environment.
The virtual environment can be customized to simulate real-world scenarios such as driving, walking, or navigating through a crowded area. The stimuli are presented aerodynamically and interactively, which can help reduce patient fatigue and improve the accuracy of results [32–
37]. It is reportedly more sensitive, [32, 33] and
equally reliable [36, 37]. VR technology can also potentially improve patient engagement and sat­isfaction with visual eld testing. Because the testing environment is more interactive and engaging, patients may be more likely to comply with testing instructions and complete the test accurately. The challenges, however, are its cost (which could be a barrier to widespread use) and technology (setting it up and calibrating it are dif­cult). Further research is needed to determine the optimal design and implementation of VR visual eld testing in clinical practice. One such device is described in Chap. 35.
21.8 Articial Intelligence (AI)
inVisual Field Measurement
In recent years, there has been growing interest in using articial intelligence (AI) to automate and improve the analysis of visual eld data. Several AI platforms have been developed [38–44]. The Advanced Glaucoma Intervention Study (AGIS) group developed the Progression Analysis Tool (PAT) that used a statistical algorithm to analyze visual eld data and detect changes over time that may indicate disease progression. More recently, deep learning algorithms have been applied to analyze visual elds in glaucoma. These algo­rithms use articial neural networks to analyze large datasets of visual eld data and identify pat­terns and trends that traditional methods may miss. One example is the Glaucoma Deep
252
A. K. Roy et al.
Learning Detection (GDD) system developed by researchers at Google. The GDD uses a deep learning algorithm to analyze visual eld data and provide an objective measure of glaucoma­tous damage.
21.8.1 Pros andCons ofAI forVisual Field inGlaucoma
Pros
1. AI may improve the accuracy and consistency
of visual eld testing, as it analyzes data objectively and without inter-observer variability.
2. AI algorithms may be able to detect early
signs of glaucomatous damage that may be missed by traditional methods, thus enabling earlier diagnosis and treatment.
3. AI algorithms can analyze large visual eld
datasets and provide detailed and nuanced information on the patient’s visual eld status.
4. The recent introduction of amalgamating
structural parameters to predict functional visual eld indices and parameters is a new development that may change how glaucoma is monitored over time.
Cons
1. AI algorithms may be less transparent and
interpretable than traditional methods, mak­ing it difcult for clinicians to understand the analysis process, which may affect their abil­ity to interpret the test results.
2. AI algorithms may be less accurate than tradi-
tional methods in certain patient populations, such as those with advanced glaucoma or those with poor xation stability.
3. Using AI algorithms may raise ethical con-
cerns about patient privacy and data security.
21.9 Pediatric Perimetry
The pediatric perimeter is a device that shows promise as a clinical device to map visual fields in infants and patients with special
needs [45]. Although a lot of research is cur­rently being done to develop pediatric perim­etry, this subject is out of the scope of this chapter.
21.9.1 The Future ofVisual Field Testing
Visual eld testing is an integral part of glaucoma management. Recent advances have improved the efciency, accessibility, and reliability of visual eld testing. The future developments include multifocal steady-state visual-evoked potentials (mfSSVEP) [46] and adaptive optics scanning laser ophthalmoscopy (AOSLO) [47,
48]. The mfSSVEP is a portable brain-computer
interface (BCI) designed to assess visual eld damage objectively. The AOSLO technology uses a wavefront sensor that records ocular wave­front aberrations and calculates the distortion degree as light passes from the cornea and retina and back into the cornea. The amount of distor­tion is calculated and corrected to give a high­resolution image captured through a CCD camera system. Additionally, novel testing modalities such as portable devices, tablets, and VR head­sets may improve the ease of testing for both, remote and in clinical settings, for patients with glaucoma and other related disorders.
Funding Hyderabad Eye Research Foundation, Hyderabad, India.
Disclosure NA.
References
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20. de Moraes CG, Hood DC, Thenappan A, etal. 24-2 visual elds miss central defects shown on 10-2 tests in glaucoma suspects, ocular hypertensives, and early glaucoma. Ophthalmology. 2017;124:1449–56.
21. Yu S, Lee GC, Callan T, etal. Comparison of SITA faster 24-2C test times to legacy SITA tests (abstract). Invest Ophthalmol Vis Sci. 2019;60:2454.
22. Phu J, Kalloniatis M. Ability of 24-2C and 24-2 grids in identifying central visual eld defects and structure- function concordance in glaucoma and sus­pects. Am J Ophthalmol. 2020;219:317–31.
23. https://www.haag- streit.com/leadmin/Haag- Streit_
USA/Diagnostics/Visual_Field_Digest_8th_eng_. pdf. Accessed 15 June 2023.
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25. “VisualFields Easy”: an iPad application as a simple tool for detecting visual eld defects. Philippine J Ophthalmol. paojournal.com. Accessed 15 June 2023.
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https://doi.org/10.1155/2020/8384509.
28. Jones PR, Campbell P, Callaghan T, etal. Glaucoma home monitoring using a tablet-based visual eld test (Eyecatcher): an assessment of accuracy and adherence over 6 months. Am J Ophthalmol. 2021;223:42–52.
29. M700 Automated Perimeter | Medmont International Pty Ltd. Accessed 15 June 2023.
30. Easyeld®- Perimetry- Highlights- OCULUS, Inc. Accessed 15 June 2023.
31. Tsapakis S, Papaconstantinou D, Diagourtas A, et al. Home-based visual eld test for glaucoma screening comparison with Humphrey perimeter. Clin Ophthalmol. 2018;12:2597–606. https://doi.
org/10.2147/OPTH.S187832.
32. Razeghinejad R, Gonzalez-Garcia A, Myers JS, Katz LJ.Preliminary report on a novel virtual reality perimeter compared with standard automated perim­etry. J Glaucoma. 2021;30:17–23.
33. Stapelfeldt J, Kucur SS, Huber N, etal. Virtual reality­based and conventional visual eld examination com­parison in healthy and glaucoma patients. Transl Vis Sci Technol. 2021;10(12):10. https://doi.org/10.1167/
tvst.10.12.10.
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36. Narang P, Agarwal A, Srinivasan M, Agarwal A. Advanced vision analyzer-virtual reality perime­ter: device validation, functional correlation and com­parison with Humphrey eld analyzer. Ophthalmol
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tvst.6.4.3.
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Retinal Nerve Fiber Layer
22
AparnaRao andNiladriB.Puhan
22.1 Introduction
The retinal nerve ber layer (RNFL) is a thin layer of nerve bers originating from the ganglion cells in the retina and converging to form the optic nerve, which serves as a conduit for visual signals from the retina to the brain [1–3]. One of the earli­est and most pathognomonic features of glaucoma is RNFL [2, 4, 5]. Structural loss of the RNFL layer precedes visual eld damage by many years; this is crucial for diagnosing pre-perimetric glau­coma and monitoring disease progression [5, 6]. Thus, RNFL is an important biomarker for glau­coma diagnosis and monitoring disease progres­sion. The RNFL thickness can be measured using a variety of techniques, including optical coher­ence tomography (OCT), scanning laser polarim­etry (SLP), and confocal scanning laser ophthalmoscopy (CSLO) [2, 5–7]. While techno­logical developments have improved the resolu­tion and accuracy of imaging modalities used for RNFL measurements, articial intelligence (AI) has emerged as a promising tool for detecting and
A. Rao (*) Kallam Anji Reddy Campus, L V Prasad Eye Institute, Hyderabad, India e-mail: aparna@lvpei.org
N. B. Puhan Indian Institute of Technology Bhubaneswar, Bhubaneswar, India e-mail: nbpuhan@iitbbs.ac.in
measuring RNFL defects in glaucoma. This chap­ter will elaborate on the anatomical characteriza­tion of the RNFL and its relevance in glaucoma while detailing methods to assess them. This chapter will also discuss using AI-based tools for RNFL assessment using fundus imaging.
22.2 History
The retinal nerve ber layer was rst described in the scientic literature in the early nineteenth century. The Italian anatomist Giovanni Battista Betti is credited with identifying optic nerve bers in animal and human retinas in 1823. In the early twentieth century, investigators could observe and study the RNFL in living human eyes using the ophthalmoscope and slit lamp. Over the following decades, researchers have used various imaging and diagnostic techniques, such as fundus photography and visual eld test­ing, to study the RNFL [5–7]. The invention of OCT in the 1990s revolutionized our ability to visualize and measure the RNFL with unprece­dented accuracy and detail. Further technological advancements, including the nerve ber layer analyzer and retinal tomography, have marked a paradigm shift in glaucoma imaging [5, 6]. In the past few years, this has been augmented expo­nentially using AI in the interpretation and analy­sis of RNFL thickness and measurements in glaucoma monitoring and progression.
© 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_22
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A. Rao and N. B. Puhan
22.3 Revisiting RNFL Anatomy andNew Insights fromTechnology
The anatomy of the RNFL is described by its location, composition, and organization [2, 3, 8–
10]. The RNFL is located just below the inner
limiting membrane (ILM) of the retina and above the ganglion cell layer (GCL) (Fig. 22.1). The RNFL consists of unmyelinated axons of the ganglion cells, which are bundled together to form the nerve ber layer [1–3, 8, 9]. The axons are surrounded by glial cells called astrocytes, which provide support and insulation. The RNFL also contains blood vessels that supply the retina with nutrients and oxygen.
22.3.1 Organization
The RNFL is thickest in the peripapillary region, which is the area around the optic nerve head, and gradually thins towards the retinal periphery [1–3, 8]. The axons in the RNFL, are organized into bundles that converge towards the optic nerve head, and exit the eye to form the optic nerve. The organization of the RNFL is also inuenced by the distribution of the ganglion cells in the retina. Different types of ganglion cells detect different types of visual information, such as color, contrast, and motion [1, 3, 8]. The
ganglion cells are not evenly distributed across the retina but are clustered in specic regions known as retinal subelds. These subelds cor­respond to different regions of the visual eld and have distinct connectivity patterns with other parts of the visual system.
In glaucoma, the loss of retinal ganglion cells and their axons occurs in a characteristic pattern. These axon bundles are aligned in a radial pattern around the optic nerve head, with the thickest bundles located in the superior and inferior regions of the retina (Fig.22.2). Knowledge of the organization of the retinal nerve ber layer (RNFL) is critical for understanding the topo­graphic localization of nerve damage in glaucoma.
22.3.2 Topographic Localization
andits Relevance inGlaucoma
Retinal ganglion cells are organized into distinct subtypes, which differ in their spatial distribution and functional properties [1–3]. Each ganglion cell subtype receives input from specic photorecep­tors and processes visual information differently. The axonal fascicles are organized in a distinct and orderly manner as they enter the optic nerve, and this topographic localization changes as they exit the eye to the lateral geniculate body and visual cortex. The retinotopic localization determines the
Fig. 22.1 Anatomy and composition of the retinal nerve ber layer. (Original image created in BIORENDER https://
www.biorender.com/ and Free AI generator)
22 Retinal Nerve Fiber Layer
257
Fig. 22.2 Structural attributes of the retinal nerve ber layer. The left panel shows the different regions of the optic nerve; the right panel shows the positions of the
spatial integrity and continuity of signals from the retina to the brain. While the bundles arch from the peripheral retina superiorly and inferiorly, the pap­illomacular bundle approaches the optic nerve directly (Fig.22.2) [1, 2, 8, 11–14]. The peripheral bers arising from the arcuate regions are located deeply in the prelaminar portion of the optic nerve, and the macular bers closer to the optic nerve are placed more supercially. This notable shift in arrangement is seen in the papillomacular bundle bers that course from the temporal part of the laminar region to the central part in the retrolami­nar portion of the optic nerve. The papillomacular bundle is organized such that the most central bers, which correspond to the fovea, are located in the center of the bundle, with progressively more peripheral bers arranged around the periph­ery of the bundle. This arrangement of macular bers and specic attributes of the ganglion cells in the macula makes the papillomacular bundle
axons from different regions of the retina as they course from the optic nerve to the brain. (Original image created in BIORENDER https://www.biorender.com/)
more susceptible to intraocular pressure (IOP)­dependent damage than ischemic/vascular damage in glaucoma [11, 14].
The other notable change is the rearrangement of the nasal and temporal bers as these travel through the optic nerve. In the retina, the nasal bers (from the nasal part of the retina) cross over to the opposite side of the brain at the optic chiasm, and the temporal bers (from the tempo­ral part of the retina) remain on the same side of the brain. After the bers cross at the optic chi­asm, the axons continue as the optic tracts, which extend from the chiasm to the brain. The axons in the optic tracts are arranged in a distinct and orderly manner, with those from the temporal retina located in the lateral portion of the tract and those from the nasal retina located in the medial portion of the tract. As the axons continue into the brain, they form synapses with neurons in the lateral geniculate nucleus of the thalamus.
258
Fig. 22.3 Location of the macular vulnerable zone, which is particularly vulnerable to early glaucoma damage
A. Rao and N. B. Puhan
The spatial arrangement of the bers typically confers different susceptibility patterns of loss in early or established glaucoma [1, 3, 11, 13, 14]. Different zones have been identied on the optic nerve head and macular regions that may be affected early in glaucoma. Of particular interest is the macular vulnerable zone [15, 16]. It is a strip of bers between 295 and 322° on the infe­rior optic disc, which results in a paracentral visual eld defect (Fig.22.3) [6, 16]. This area is most vulnerable to damage and contains axons from the temporal part of the inferior macula that projects towards the optic nerve above the infe­rior arcuate bers. An RNFL defect in this region should alert the clinician to the possibility of cen­tral involvement, requiring 10-2 visual elds for documenting early glaucomatous defects.
22.4 Techniques ofRNFL
Detection andQuantication
There are several ways to detect and quantify the RNFL in glaucoma [6, 7, 11, 12, 17–24]. Some of the most common techniques are described below:
22.4.1 Fundus Slit Lamp Biomicroscopy
This is the most common method currently used in clinics. The RNFL defect is visible as a wedge­shaped dark area with loss of normal white stria­tions, fanning out from the optic disc towards the arcuate area, and may sometimes reach close to the macula [1–3, 17–19].
Technique: While using a +78 or +90D lens on the slit lamp, ensure the patient is seated comfort­ably, and focus the view on the optic disc margins superiorly and inferiorly. Now move the gaze towards the vessels along the supertemporal and inferotemporal arcade and look for disruption of the normal “light-dark-light” pattern of RNFL striations. In the presence of an RNFL defect, this pattern is interrupted by a focal or diffuse dark area along the superior or inferior arcades. Once identied, follow the dark pattern and mark its location with respect to its proximity to the fovea and adjacent vessel trunks for visual quantica­tion and clinical drawings (Fig.22.4).
Clinical application: A meticulous hand­drawing of the extent and size of the RNFL defect can be very useful in low-resource settings where
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Fig. 22.4 Example of hand drawings from clinical slit lamp biomicroscopy to denote the exact location and extent of RNFL defects and their distance from landmarks like vessels or fovea. The right panel now shows the incor-
other imaging modalities or fundus photography are unavailable (Fig.22.4).
22.4.2 Fundus Photography
Fundus photography uses a specialized camera to obtain high-resolution retinal images.
Clinical application: By analyzing the images, fundus photography can provide measurements of RNFL thickness and overall health [1, 3, 17–
19]. This is an economical way for low-resource
countries where all imaging modalities may not be available. These photographs can be two- or three dimensional; the latter is used for stereo­scopic view. These can also be used for analysis using software like Image J for analyzing vessel diameter, rim/disc area, torsion, or any other quantitative fundus parameters. Figure 22.5 shows representative non-stereoscopic fundus pictures depicting focal or diffuse RNFL defects detectable using the above technique.
22.4.3 Optical Coherence
Tomography (OCT)
OCT is widely used to measure RNFL thickness in glaucoma.
Technology: OCT is a non-invasive imag­ing technique that uses light waves to create high- resolution retina images. It is based on
rect (blue cross) and the correct method (yellow tick) of hand-drawing for depicting rim ndings and the location/ extent of nerve ber layer defects
the principle of low-coherence light interfer­ometry [6, 7].
Clinical application: OCT provides precise measurements of the thickness of the RNFL in different regions of the retina. Spectral-domain OCT (SD-OCT) is more popular than time­domain OCT (TD-OCT) owing to its superior axial resolution (3–6 μm versus 8–10 μm with TD-OCT) while enabling 3D volumetric analysis of the optic nerve.
22.4.4 Scanning Laser Polarimetry
(SLP)
Technology: SLP (GDx, Carl Zeiss Meditec, Dublin, CA) uses a laser to measure the polariza­tion of light as it passes through the RNFL and utilizes the birefringent nature of the RNFL [3, 6,
7, 21–24]. By analyzing the polarization patterns
and extent of retardation as the light passes through the RNFL of varying thickness, SLP measures RNFL and the overall extent of optic nerve damage. Recent versions (GDx VCC [Variable Corneal Compensator], GDx ECC [Enhanced Corneal Compensator], and GDx PRO) now have adjusted for and provided com­pensations for birefringence of the cornea and macula for superior reproducible measurements of RNFL thickness. The RNFL thickness mea­sured by this instrument is currently more reli­able than other devices.
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A. Rao and N. B. Puhan
Fig. 22.5 Fundus images of patients with retinal nerve ber layer defects. Note the focal or diffuse dark-colored and wedge-shaped defect with the prominence of the ves-
22.4.5 Confocal Scanning Laser Ophthalmoscopy (CSLO)
The CSLO (Heidelberg Retina Tomograph, HRT, Heidelberg Engineering, Heidelberg, Germany) uses a laser to create high-resolution images of the optic nerve head and RNFL [3, 21, 22, 24–
26]. It provides measurements of RNFL thick-
ness across different regions of the optic nerve and a volumetric three-dimensional topography of the optic nerve.
Clinical application: CSLO could detect RNFL changes longer than 5years before evident visual eld damage in the ocular hypertension treatment study [22, 23]. The newer versions have expanded the normative database with improved accuracy and analytical tools that help discriminate between healthy and glaucomatous eyes with the glaucoma probability score (GPS).
Limitations: While most of the above imaging modalities help quantitate optic nerve parameters (and macula in OCT) and provide a means for objective monitoring of the disease progression, the progression algorithms using imaging modali­ties raise concerns about the utility of the structural information vis-a-vis the clinical treatment para­digms. These are also subject to errors by increased
sels that appear darker with contrasting background cho­roidal tessellation
“RNFL thickness” reported by the instrument owing to surface gliosis and hence cannot be applied to patients with media opacities or high refractive errors [22–26]. These devices are not feasible for bedridden patients. Additionally, the information or analysis these machines applies only to people of age and race included in the nor­mative database [6, 7, 19, 22, 23, 26]. Though these are widely reported to have utility in clinics, their sensitivity decreases as the specicity increases [26]. The discriminatory abilities using the best parameters of all these instruments have been reported to be similar and comparable to those of subjective evaluation by glaucoma experts. The structure–function correlation is bet­ter with the SD-OCT than with SLP or CSLO.
22.5 Articial Intelligence-Based Early RNFL Defect Methodologies
The past decade has seen a huge increase in AI (Articial Intelligence)-based tools for RNFL defect detection and quantication using fundus images and AI-based analysis of optic nerve head parameters [27–42].
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