Confocal Scanning Laser Ophthalmoscopy Research Articles

Progression, reliability, predicting parameters and sample size calculations for quantitative fundus autofluorescence measures in ABCA4-related retinopathy

Background/aimsTo investigate the progression of quantitative autofluorescence (qAF) measures and the potential as clinical trial endpoint in ABCA4-related retinopathy.MethodsIn this longitudinal monocentre study, 64 patients with ABCA4-related retinopathy (age (mean±SD),...

Impact of Acute Ocular Hypertension on Retinal Ganglion Cell Loss in Mice.

To assess the correlation between intraocular pressure (IOP) levels and retinal ganglion cell (RGC) loss across different fixed-duration episodes of acute ocular hypertension (AOH). AOH was induced in Thy1-YFP-H transgenic mice by inserting a needle connected to a saline solution container into the anterior chamber. Thirty-one groups were tested, each comprising three to five mice exposed to IOP levels ranging from 50 to 110mm Hg in 5/10mm Hg increments for 60/90/120minutes and a sham control group. The YFP-expressing RGCs were quantified by confocal scanning laser ophthalmoscopy, whereas peripapillary ganglion cell complex thickness was measured using spectral-domain optical coherence tomography. Changes in RGC count and GCCT were determined from values measured 30days after AOH relative to baseline (before AOH). In the 60-minute AOH groups, RGC loss varied even when IOP was increased up to 110mm Hg (36.8%-68.2%). However, for longer durations (90 and 120minutes), a narrow range of IOP levels (60-70mm Hg for 90-minute duration; 55-65mm Hg for 120-minute duration) produced a significant difference in RGC loss, ranging from <25% to >90%. Additionally, loss of YFP-expressing RGCs was comparable to that of total RGCs in the same retinas. Reproducible RGC loss during AOH depends on precise durations and IOP thresholds. In the current study, the optimal choice is an AOH protocol set at 70mm Hg for a duration of 90minutes. This study can assist in determining the optimal duration and intensity of IOP for the effective utilization of AOH models.

MODERN METHODS FOR DIAGNOSTICS OF GLAUCOMA

Glaucoma is a chronic optical neuropathy characterized by loss of ganglion cells with the development of specific changes in the optic nerve head (ONH) and retinal nerve fiber layer (RNFL). Early detection of the disease plays an important role in preventing the development structural abnormalities and irreversible vision loss. Diagnosis of glaucoma is based on assessing the safety of optic nerve structures and visual functions results clinical examination of the optic disc and RNFL are subjective and vary greatly. In connection with this, research in recent years has been aimed at developing additional objective methods for diagnosing glaucoma. The possibility of using confocal scanning laser ophthalmoscopy, scanning laser perimetry and optical coherence tomography to assess the condition of the optic disc. In order to ensure early detection of visual field defects options are currently being considered replacing standard automated perimetry (SAP) with selective, which includes short-wavelength automated perimetry (SWAP) and perimetry with the illusion of spatial doubling frequencies (frequency-doubling technology perimetry, FDT). The article is an overview modern method for diagnosing glaucoma in the context of their use in clinical practice.

Characterization of a new fluorescence lifetime imaging ophthalmoscope

Aims/Purpose: Fluorescence lifetime imaging ophthalmoscopy (FLIO) allows in vivo measurement of autofluorescence intensity decays of endogenous fluorophores in the ocular fundus. So far, only devices from Heidelberg Engineering based on the Spectralis system have been used in FLIO research. Here, we present and characterize a new FLIO device based on the RETImap system from Roland Consult.Methods: The device is based on a confocal scanning laser ophthalmoscope (35° field, 512 × 512 px). A ps diode laser (BDL‐SMN 473 nm, Becker &amp; Hickl GmbH, Berlin, Germany) excites autofluorescence. The fluorescence photons are split into a short (498–560 nm, SSC) and a long (560–720 nm, LSC) spectral channel (one HPM‐100‐40 detector [Becker &amp; Hickl GmbH] each) and are detected by time‐correlated single photon counting (SPC‐160, Becker &amp; Hickl GmbH). We determined the maximum laser power (ILT2400, International Light Technologies, Inc. Peabody, MA, USA) and analysed the instrument's behaviour at three different laser power levels (150 μW, 200 μW and max.) in terms of laser spectrum (CAS140CT, Instrument Systems GmbH, Munich, Germany) and instrument response function (IRF). The IRF was determined using a 25 μM Eosin Y solution, mixed with a 5 M solution of potassium iodide, placed in a flat cuvette (110‐OS, Hellma GmbH &amp; Co. KG, Müllheim, Germany) in front of the objective lens of the FLIO device. Fluorescence measurements of approximately 1‐min duration were performed three times for all three laser powers. The IRF and the full width at half maximum (FWHM) were calculated using FLIMX software (www.flimx.de).Results: The max. laser power was 280 μW. The peak wavelengths of the laser spectra were 467.6 (150 μW), 467.9 (200 μW) and 468.0 nm (280 μW). IRF FWHM in SSC were 298.6 ± 1.1 ps (150 μW), 341.0 ± 2.5 ps (200 μW) and 347.5 ± 6.0 ps (280 μW). In LSC, the IRF FWHM were 290.4 ± 3.8 ps (150 μW), 344.0 ± 3.4 ps (200 μW) and 358.8 ± 1.3 ps (280 μW). Results are mean ± standard deviation.Conclusions: A new fluorescence lifetime imaging ophthalmoscope has been characterized. The device offers a high laser power for fluorescence excitation, a large field of view, a high spatial resolution, and a sufficiently high time resolution. Thus, it is suitable for fluorescence lifetime studies.

Scanning laser ophthalmoscopy ‐ Basic optical principles

Scanning Laser Ophthalmoscopy (SLO) produces an ocular fundus image by moving a focused laser beam via scanning mirrors over the retina in a grid pattern and registering the reflected light from each scanned point. In confocal SLO, a pinhole is placed in front of the detector to cut off scattered or defocused light coming from outside the point of interest, which otherwise can blur the image. This results in a focused, high‐contrast image of a single tissue layer located at the focal plane. Tomographic information can be extracted by moving the plane of the pinhole. The use of various wavelengths allows for different applications, such as fluorescein angiography, indocyanine green angiography, and autofluorescence imaging. This lecture will present the basic principles of SLO, as well as discuss notable applications and variants of the technology.

What artificial intelligence can bring us in the diagnosis and progression of glaucoma

Aims/Purpose: Our goal is to focus on the contribution of artificial intelligence in glaucoma from diagnosis to progression.Methods: By a literature review, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression.Results: For the diagnosis: Recently, two papers were published reporting results of DL algorithms to diagnose glaucoma from Visual Field data. Several authors have evaluated AI‐based fundus photograph analysis for its utility for detecting glaucoma such as the study of Liu et al which grouped 241 032 images of 68 013 patients with a sensitivity of 96.2% and specificity of 87.2% also for analysing OCT imaging data from peripapillary RNFL thickness maps and the macular ganglion cell complex for discriminating between glaucomatous and normal eyes with AROC values ranging from 0.69 to 0.99. Prediction of IOP trends from previous data and medications would be a useful and plausible use of AI. For the progression of glaucoma: Multiclass support vector machines (SVMs), have been used to simultaneously discriminate between normal, non progressing, and progressing eyes through the analysis of confocal scanning laser ophthalmoscopy (CSLO) images with a correct classification rate of 88% .Conclusions: Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. Research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.

Application of artificial intelligence in glaucoma

Glaucoma is the mainly cause of irreversible blindness characterized with structural changes of optic nerve head, retinal ganglion cell death and loss of visual acuity, with estimated 64.3 million patients aged from 40 to 80 worldwide. When patients seek medical advice because of visual acuity, glaucoma often has advanced to late stage. One major challenge is to identify large number of undiagnosed patients. The reason why screening programs are not employed regularly is that misdiagnoses can be made due to large amounts of false positives in the procedure.Artificial intelligence (AI) has brought new breakthroughs for automated screening for glaucoma，which can perform a task by classifying and identifying data without human intervention, including supervised and unsupervised machine learning two categories. Due to the limitation of manually designed features and disability in detection for high dimensionality of optic disc images, advances in techniques for evaluation of glaucoma came with the use of segmentation‐free approaches. We reported a deep learning system and assessed its generalizability in various data sets, with similar patterns in sensitivity (82.2%–96.1%) and specificity (70.4%–97.1%).Visual field data presents low dimensionality and high noise compared to optic disc images, whose hidden patterns can be detected by unsupervised learning better. Two most reported unsupervised algorithms were clustering and component analysis. Such technologies adopted in glaucoma screening have significant potential to increase accessibility in remote areas and decrease the cost for widely screening. Further studies similarly demonstrated that machine learning can perform comparable to or over human experts in mean deviation, pattern standard deviation and glaucoma hemifield test. A back‐propagation neural network was even reported to successfully detect visual field progression with Area Under Curve (AUC) of 0.92. Further advances were also made through algorithms based on CNN showing both higher sensitivity and specificity than traditional machine learning method.In terms of optical coherence tomography (OCT), early studies based on MLC using TD‐OCT showed an accuracy not worse than non‐artificial intelligence analysis. The latest OCT technologies, including SD‐OCT and SS‐OCT, combined with deep learning are reported more sensitive for early glaucoma detection, with a high AUC up to 0.937. Recently a study reported the ability of their model to detect RNFL thickness from OCT with the AUC of 0.944. Recent years, more parameters have been put in including OCT RNFL thickness, standard automated perimetry and confocal scanning laser ophthalmoscopy imaging, making an improvement in glaucoma detection.However, there remains several limitations and further studies is required. First, the exact mechanism how DL algorithms evaluate features and make predictions remains unknown, which is called ‘black boxes’. Another limitation is that it can be difficult for DL to classify glaucoma in eyes with less severe disease manifestations or multiple comorbid eye conditions, especially high myopia, which asks for a large images database. Furthermore, in order to develop a more dependent screening method, other clinical parameters including intraocular pressure and central corneal thickness should be integrated, and application and validation of such in a real‐world screening need additional research to support.

Research Progress and Application of Confocal Scanning Laser Ophthalmoscope (Invited)

Research Progress and Application of Confocal Scanning Laser Ophthalmoscope (Invited)

QUANTITATIVE AUTOFLUORESCENCE IN CENTRAL SEROUS CHORIORETINOPATHY.

Central serous chorioretinopathy (CSC) is associated with pachychoroid and dysfunctional retinal pigment epithelium. Autofluorescence (AF) is typically altered. The authors performed this study to quantify these alterations using quantitative AF (qAF) in patients with CSC and in their fellow eye in comparison with a healthy control group. Patients with CSC and healthy controls were recruited prospectively. All patients received a full clinical examination including best-corrected visual acuity, enhanced depth imaging-optical coherence tomography, and qAF. Quantitative autofluorescence images were taken with a confocal scanning laser ophthalmoscope (Heidelberg Engineering). Quantitative autofluorescence values were assessed in specified regions of the inner eight and the middle ring of the Delori grid. In total, 141 eyes of 77 patients with CSC were included. Ninety eyes had a manifest CSC (group 1) while 51 fellow eyes (group 2) did not show signs of CSC. There were no significant differences of qAF values between these two groups: mean qAF values were 241.3 (inner eight) and 212.8 (middle ring) in group 1 and 235.9 (inner eight) and 210.0 (middle ring) in group 2 ( P = 1.0 and 1.0). We compared these eyes with healthy controls comprising 39 eyes. Quantitative autofluorescence signals (inner eight: 164.7; middle ring: 148.9) differed significantly compared with both CSC manifest ( P < 0.001) and fellow eyes ( P < 0.001). Our results show that patients with CSC have increased qAF values in both eyes with manifest CSC and asymptomatic, clinically unremarkable fellow eyes in comparison with healthy controls. This finding suggests that qAF alterations are present even before clinical signs can be observed.

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm

PurposeThis study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images.MethodsImages were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus—Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box.ResultsA total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001).ConclusionPeripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology.

A leading cause of irreversible vision loss, glaucoma needs early detection for effective management. Intraocular Pressure (IOP) is a significant risk factor for glaucoma. Convolutional Neural Networks (CNN) demonstrate exceptional capabilities in analyzing retinal fundus images, a non-invasive and cost-effective imaging technique widely used in glaucoma diagnosis. By learning from large datasets of annotated images, CNN can identify subtle changes in the optic nerve head and retinal structures indicative of glaucoma. This enables early and precise glaucoma diagnosis, empowering clinicians to implement timely interventions. CNNs excel in analyzing complex medical images, detecting subtle changes indicative of glaucoma with high precision. Another valuable diagnostic tool for glaucoma evaluation, Optical Coherence Tomography (OCT), provides high-resolution cross-sectional images of the retina. CNN can effectively analyze OCT scans and extract meaningful features, facilitating the identification of structural abnormalities associated with glaucoma. Visual field testing, performed using devices like the Humphrey Field Analyzer, is crucial for assessing functional vision loss in glaucoma. The integration of CNN with retinal fundus images, OCT scans, visual field testing, and IOP measurements represents a transformative approach to glaucoma detection. These advanced technologies have the potential to revolutionize ophthalmology by enabling early detection, personalized management, and improved patient outcomes. CNNs facilitate remote expert opinions and enhance treatment monitoring. Overcoming challenges such as data scarcity and interpretability can optimize CNN utilization in glaucoma diagnosis. Measuring retinal nerve fiber layer thickness as a diagnostic marker proves valuable. CNN implementation reduces healthcare costs and improves access to quality eye care. Future research should focus on optimizing architectures and incorporating novel biomarkers. CNN integration in glaucoma detection revolutionizes ophthalmology, improving patient outcomes and access to care. This review paves the way for innovative CNN-based glaucoma detection methods. Abbreviations: CNN = Convolutional Neural Networks, AI = Artificial Intelligence, IOP = Intraocular Pressure, OCT = Optical Coherence Tomography, CLSO = Confocal Scanning Laser Ophthalmoscopy, AUC-ROC = Area Under the Receiver Operating Characteristic Curve, RNFL = Retinal Nerve Fiber Layer, RNN = Recurrent Neural Networks, VF = Visual Field, AP = Average Precision, MD = Mean Defect, sLV = square-root of Loss Variance, NN = Neural Network, WHO = World Health Organization.

Retina Oculomics in Neurodegenerative Disease.

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging technologies including electroretinography, optical coherence tomography (OCT), confocal scanning laser ophthalmoscopy, fluorescence lifetime imaging ophthalmoscopy, and OCT angiography have revolutionized the ability to understand systemic diseases and even detect them earlier than clinical manifestations for earlier intervention. With the advent of increasingly large ophthalmic imaging datasets, machine learning models can be integrated into these ocular imaging biomarkers to provide further insights and prognostic predictions of neurodegenerative disease. In this manuscript, we review the use of ophthalmic imaging to provide insights into neurodegenerative diseases including Alzheimer Disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, and Huntington Disease. We discuss recent advances in ophthalmic technology including eye-tracking technology and integration of artificial intelligence techniques to further provide insights into these neurodegenerative diseases. Ultimately, oculomics opens the opportunity to detect and monitor systemic diseases at a higher acuity. Thus, earlier detection of systemic diseases may allow for timely intervention for improving the quality of life in patients with neurodegenerative disease.

Metabolic changes and retinal remodeling in Heterozygous CRX mutant cats (CRXRDY/+)

CRX is a transcription factor essential for normal photoreceptor development and survival. The CRXRdy cat has a naturally occurring truncating mutation in CRX and is a large animal model for dominant Leber congenital amaurosis. This study investigated retinal remodeling that occurs as photoreceptors degenerate. CRXRdy/+ cats from 6 weeks to 10 years of age were investigated. In vivo structural changes of retinas were analyzed by fundus examination, confocal scanning laser ophthalmoscopy and spectral domain optical coherence tomography. Histologic analyses included immunohistochemistry for computational molecular phenotyping with macromolecules and small molecules. Affected cats had a cone-led photoreceptor degeneration starting in the area centralis. Initially there was preservation of inner retinal cells such as bipolar, amacrine and horizontal cells but with time migration of the deafferented neurons occurred. Early in the process of degeneration glial activation occurs ultimately resulting in formation of a glial seal. With progression the macula-equivalent area centralis developed severe atrophy including loss of retinal pigmentary epithelium. Microneuroma formation occured in advanced stages as more marked retinal remodeling occurred. This study indicates that retinal degeneration in the CrxRdy/+ cat retina follows the progressive, phased revision of retina that have been previously described for retinal remodeling. These findings suggest that therapy dependent on targeting inner retinal cells may be useful in young adults with preserved inner retinas prior to advanced stages of retinal remodeling and neuronal cell loss.

Multimodal Deep Learning Classifier for Primary Open-angle Glaucoma Diagnosis Using Wide-field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

An OCT-based multimodal deep learning classification model including texture information is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia. To evaluate the diagnostic accuracy of a multimodal deep learning (DL) classifier using wide optical coherence tomography (OCT) optic nerve head cube scans in eyes with and without axial high myopia. 371 primary open-angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia (axial length AL≤26mm) and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL>26mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: 1) texture-based en face image, 2) retinal nerve fiber layer (RNFL) thickness map image and 3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves (AUROC) were used to compare model accuracy. Adjusted AUROC for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models (0.83 [0.79, 0.86] for texture based en face image; 0.84 [0.81, 0.87] for RNFL thickness map; and 0.68 [0.61, 0.74] for cSLO image; all P≤0.05). Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy (0.89 [0.86 , 0.92]) compared to texture en face image (0.83 [0.78 , 0.85]), RNFL (0.85 [0.81 , 0.86]) and cSLO image models (0.69 [0.63 , 0.76]) (all P≤0.05). Combining OCT-based RNFL thickness maps with texture based en face images showed better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.

Retromode Scanning Laser Ophthalmoscopy for Choroidal Nevi: A Preliminary Study.

The purpose of the present study was to document pathological findings on retromode imaging in choroidal nevi and evaluate its diagnostic validity, using the confocal scanning laser ophthalmoscope Nidek Mirante (cSLO). A total of 41 choroidal nevi from 41 patients were included. All patients underwent multicolor fundus (mCF), infrared reflectance (IR), green fundus autofluorescence (FAF), dark-field (DF) and retromode (RM) imaging and optical coherence tomography (OCT) scans. We investigated retromode images to evaluate choroidal nevus features by comparing the results with those of mCF, IR, FAF, DF and OCT. In 100% of available images, retromode scanning laser ophthalmoscopy was able to detect choroidal nevi with a characteristic "hypo-retro-reflective" pattern, even the cases not visible on mCF, IR and FAF images. It also made it possible to delineate the margins of lesions with the highest rate of sharpness and accuracy among the imaging modalities examined. These findings seem to demonstrate how RM-SLO is an innovative diagnostic tool to detect and follow up choroidal nevi in a fast, reliable and non-invasive way.

New Terminology and Methodology for the Assessment of the Vitreous, Its Floaters and Opacities, and Their Effect on Vision: Standardized and Kinetic Anatomical and Functional Testing of Vitreous Floaters and Opacities (SK VFO Test)

To introduce VFO and SK VFO Test: new, more representative terminology for symptoms of vitreous floaters/opacities (VFO) and new standardized kinetic (SK) anatomical-functional assessment. Eight eyes underwent before-after limited vitrectomy (LV): best-corrected visual acuity, low-luminance visual acuity, Minnesota Low Vision Reading Chart near visual acuity in logMAR, contrast sensitivity function (CV1000E), and straylight measurements (SM) (HDA/LDA/C-Quant). SK infrared confocal scanning laser ophthalmoscopy (SK IRcSLO) and swept-source widefield optical coherence tomography (SS-WF-OCT) identified VFO and posterior vitreous detachment (PVD). SK IRcSLO was performed with gaze directed towards the extreme superior, inferior, and lateral directions. Anatomical-functional results after LV improved in 7 eyes (87.5%): objective scatter index (27.34%), disturbance index (47.97%) and C-Quant Log units (2.26%). Pre-LV SK IRcSLO and SS-WFOCT imaging identified dynamic well-defined VFO and PVD status (100%). A residual asymptomatic anterior cortical vitreous-induced shadowing ripple effect was detected post-LV. This is the first objective-standardized test accounting for VFO kinesis and intermittent effect. There is potential for personalizing treatment and establishing best candidates for laser or surgery. [Ophthalmic Surg Lasers Imaging Retina 2023;54:306-315.].

A comparison of optophysiological biomarkers of photoreceptor stress and phototoxicity in BALB/cJ, B6 (Cg)-Tyrc-2J/J, and C57Bl/6J mouse strains.

Ophthalmic imaging instruments, including the confocal scanning laser ophthalmoscope and spectral-domain optical coherence tomography system, originally intended for revealing ocular microstructures in the human eye, have been deployed by vision researchers to evaluate the eyes of numerous small and large animal species for more than two decades. In this study, we have used these two instruments to obtain imaging data sequentially from the retinas of three prominent, widely used experimental mouse models to document changes induced by two contrasting vivarium lighting conditions. Mice studied include albino BALB/cJ and B6(Cg)-Tyrc-2J/J and pigmented C57Bl/6J. Mice were reared under dim light conditions until ~8 weeks of age where they underwent baseline imaging. Following, mice were returned to the dim vivarium or relocated to the top rack cage position in a standard vivarium. Mice were then followed for several months by ocular imaging to catalog the retinal dynamics as a function of long-term dim vs. elevated, standard vivarium lighting exposure levels. Upon exposure to elevated light levels, B6(Cg)-Tyrc-2J/J underwent similar changes as BALB/cJ in regard to photoreceptor outer segment shortening, photoreceptor layer proximal aspect hyperreflective changes, and the development of retinal infoldings and autofluorescent sub-retinal inflammatory monocyte infiltrate. Noteworthy, however, is that infoldings and infiltrate occurred at a slower rate of progression in B6(Cg)-Tyrc-2J/J vs. BALB/cJ. The photoreceptor outer nuclear layer thickness of BALB/cJ degenerated steadily following elevated light onset. In contrast, B6(Cg)-Tyrc-2J/J degeneration was unremarkable for many weeks before experiencing a noticeable change in the rate of degeneration that was concomitant with a plateau and decreasing trend in number of retinal infoldings and monocyte infiltrate. Pathological changes in C57Bl/6J mice were unremarkable for all imaging biomarkers assessed with exception to autofluorescent sub-retinal inflammatory monocyte infiltrate, which showed significant accumulation in dim vs. elevated light exposed mice following ~1 year of observation. These data were evaluated using Spearman's correlation and Predictive Power Score matrices to determine the best imaging optophysiological biomarkers for indicating vivarium light stress and light-induced photoreceptor degeneration. This study suggests that changes in proximal aspect hyperreflectivity, outer segment shortening, retinal infoldings and autofluorescent sub-retinal inflammatory monocyte infiltrate are excellent indicators of light stress and light-induced degeneration in albino B6(Cg)-Tyrc-2J/J and BALB/cJ mouse strains.

Iris angiography in ADAMTS10 mutant dogs with open-angle glaucoma (ADAMTS10-OAG).

To evaluate anterior segment angiographic findings in hypertensive ADAMTS10-open-angle glaucoma (ADAMTS10-OAG) eyes as compared to normotensive control eyes. Nine ADAMTS10-OAG beagles and four wild-type control dogs. Anterior segment angiography was performed under general anesthesia following intravenous injection of indocyanine green (ICG; 1 mg/kg) and sodium fluorescein (SF; 20 mg/kg) using a Heidelberg Spectralis® confocal scanning laser ophthalmoscope. Time to onset of iridal angiographic phases and the presence/severity of dye leakage into the iris stromal and/or aqueous humor were recorded. Group findings were compared, and multiple linear regression analysis was performed to identify potential factor associations with disease status. Time to onset of all angiographic phases visualized using ICG was significantly prolonged while time to onset of SF leakage into the aqueous humor was significantly reduced in glaucomatous eyes compared to controls. Only glaucomatous eyes (n=9) demonstrated evidence of SF stromal leakage. Mean intraocular pressure (IOP) and age were significantly higher, while mean cardiac pulse was significantly lower in glaucomatous eyes compared to controls. Blood pressure and ocular perfusion pressure were not significantly different between groups. Multiple linear regression analysis, controlling for age, IOP, and pulse demonstrated glaucoma, was not predictive of the time to onset of any angiographic phase, stromal, or aqueous humor leakage. However, pulse was a significant factor contributing to the severity of aqueous humor leakage. A compromised vascular supply to the anterior segment exists in dogs with ADAMTS10-OAG. These observations warrant further exploration of what role altered perfusion and/or disruption to the blood-aqueous barrier may play.

An optimized method for retrograde labelling and quantification of rabbit retinal ganglion cells

Rabbits are a commonly used animal model in glaucoma research, but their application has been limited by the techniques used to assess optic nerve injury (ONI). Our study devised an optimized method for retrograde labelling and analysing rabbit retinal ganglion cells (RGCs). This method involved improvements over the conventional method regarding the stereotaxic device, the positioning of superior colliculi, the target of axonal tracer delivery, and the visualization and analysis of labelled RGCs. To evaluate its efficacy, eight New Zealand White rabbits were divided into naïve and ONI groups. Unilateral limbal buckling surgery was performed in each animal of the ONI group to induce chronic ocular hypertension (OHT). The animals of both groups were injected with indocyanine green (ICG) into the interstice between the superior colliculus and occipital lobe on each side of the brain, and their eyes were examined by confocal scanning laser ophthalmoscopy (CSLO) after 48 h. The acquired images were then analysed to quantify the number of ICG-labelled RGCs in these eyes and their loss induced by OHT. To verify the identity and changes of the labelled RGCs, the retinas of the rabbits were subjected to immunofluorescence analyses. In addition, three animals were subjected to a second ICG labelling after 12 months to determine the influence of this procedure on the long-term viability of the labelled RGCs. Our results showed that ICG-labelled RGCs were detected by CSLO throughout the retinas of all animals. These RGCs showed a distinctly higher density below the ONH and were defective in sectorial areas in OHT eyes. Their average number in the cell counting area was 3989.2 ± 414.2 and 4023.3 ± 603.4 in the right and left eyes, respectively, of the naïve animals and 2590.9 ± 1474.2 and 3966.7 ± 24.0 in the OHT and non-OHT eyes, respectively, of the ONI animals. Immunofluorescence analyses showed positive staining with Brn3a and RBPMS in the ICG-labelled RGCs and sectorial defects of the cells in the OHT eyes, similarly as observed by CSLO. The second ICG labelling after 12 months in three animals showed no appreciable changes in RGC density compared with the first one. In summary, the optimized method of rabbit RGC retrograde labelling is reliable and accurate in both naïve and ONI animals and offers an approach for longitudinal observation of RGCs in the same eyes, which suggests its potential as a powerful tool for glaucoma and optic nerve research.

Retro-mode imaging of gravitational subretinal fluid associated with optic disc pit maculopathy with extensive retinal schisis in a young myopic patient

A 32-year-old female with high myopia and optic disc pit maculopathy associated with schisis was imaged on the Mirante confocal scanning laser ophthalmoscope (Nidek Co, Ltd, Gamagori, Japan). Scanning laser ophthalmoscope colour fundus photography (A) shows a hypopigmented area corresponding with the schisis (green arrows), whereas the blue-light fundus autofluorescence (B) highlights an oval hyperautofluorescent area consistent with the subretinal fluid (SRF; white arrow), which is a useful landmark to monitor the extension and localization of the fluid during the course of follow-up. Retro-mode illumination (C) demonstrates enhanced visualization of the margin of the SRF (white arrow). The myopic schisis is highlighted on retro-mode illumination (green arrows) as fine radiating lines. Optical coherence tomography demonstrates the schisis associated with subretinal fluid (D). Panel E illustrates more subtle, smaller areas of schisis adjacent to the SRF.