ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION.

PURPOSE: The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans. METHODS: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories. RESULTS: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021). CONCLUSION: These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Retinal structural changes after cessation of pentosan polysulfate sodium.

PURPOSE: To quantify baseline and longitudinal structural changes post-cessation in pentosan polysulfate sodium (PPS) retinopathy patients. METHODS: This is a retrospective cohort study. Retinal thickness and volume of choroidal and hyperreflective retinal pigment epithelium (RPE) excrescences were manually segmented from optical coherence tomography (OCT) volume scans. Baseline measurements were compared against age-matched controls. Longitudinal measurements were performed on patients with follow-up data. RESULTS: Twenty-four eyes of 13 patients were included. At baseline, the mean total retinal thickness was lower in the PPS retinopathy cohort than in age and sex-matched controls (269.1 µm vs. 290.2 µm, p = 0.006).The median (range) of follow-up was 18.6 (4.1 to 34.7) months, with the mean last follow-up of 35.2 months after cessation. During the follow-up period, the thickness of the retina decreased significantly by 11.3 µm (CI: 16.8, 5.8) (p<0.001), with an annual mean decrease of 6.70 µm. However, the mean hyperreflective RPE excrescence volume did not change significantly (p = 0.140) over the follow-up period. CONCLUSIONS: Following PPS discontinuation, although RPE excrescence volumes do not change significantly in volume, there continues to be a progressive long-term thinning of the retina which continues at a rate greater than that associated with normal aging. Consequently, long-term follow-up is suggested to monitor patients with PPS maculopathy.

Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.

BACKGROUND: Retinitis pigmentosa (RP) represents a group of progressive, genetically heterogenous blinding diseases. Recently, relationships between measures of retinal function and structure are needed to help identify outcome measures or biomarkers for clinical trials. The ability to align retinal multimodal images, taken on different platforms, will allow better understanding of this relationship. We investigate the efficacy of artificial intelligence (AI) in overlaying different multimodal retinal images in RP patients. METHODS: We overlayed infrared images from microperimetry on near-infra-red images from scanning laser ophthalmoscope and spectral domain optical coherence tomography in RP patients using manual alignment and AI. The AI adopted a two-step framework and was trained on a separate dataset. Manual alignment was performed using in-house software that allowed labelling of six key points located at vessel bifurcations. Manual overlay was considered successful if the distance between same key points on the overlayed images was ≤1/2°. RESULTS: Fifty-seven eyes of 32 patients were included in the analysis. AI was significantly more accurate and successful in aligning images compared to manual alignment as confirmed by linear mixed-effects modelling (p < 0.001). A receiver operating characteristic analysis, used to compute the area under the curve of the AI (0.991) and manual (0.835) Dice coefficients in relation to their respective 'truth' values, found AI significantly more accurate in the overlay (p < 0.001). CONCLUSION: AI was significantly more accurate than manual alignment in overlaying multimodal retinal imaging in RP patients and showed the potential to use AI algorithms for future multimodal clinical and research applications.

REPRODUCIBILITY OF MACULAR PIGMENT OPTICAL DENSITY MEASUREMENT BY TWO-WAVELENGTH AUTOFLUORESCENCE IN A CLINICAL SETTING.

PURPOSE: Macular pigment, composed of lutein, zeaxanthin, and meso-zeaxanthin, is postulated to protect against age-related macular degeneration, likely because of filtering blue light and its antioxidant properties. Macular pigment optical density (MPOD) is reported to be associated with macular function evaluated by visual acuity and multifocal electroretinogram. Given the importance of macular pigment, reliable and accurate measurement methods are important. The main purpose of this study is to determine the reproducibility of MPOD measurement by two-wavelength autofluorescence method using scanning laser ophthalmoscopy. METHODS: Sixty-eight eyes of 39 persons were enrolled in the study, including 11 normal eyes, 16 eyes with wet age-related macular degeneration, 16 eyes with dry age-related macular degeneration, 11 eyes with macular edema due to diabetic mellitus, branch retinal vein occlusion or macular telangiectasia, and 14 eyes with tractional maculopathy, including vitreomacular traction, epiretinal membrane, or macular hole. MPOD was measured with a two-wavelength (488 and 514 nm) autofluorescence method with the Spectralis HRA + OCT after pupil dilation. The measurement was repeated for each eye 10 minutes later. The analysis of variance and Bland-Altman plot were used to assess the reproducibility between the two measurements. RESULTS: The mean MPOD at eccentricities of 1° and 2° was 0.36 ± 0.17 (range: 0.04-0.69) and 0.15 ± 0.08 (range: -0.03 to 0.35) for the first measurement and 0.35 ± 0.17 (range: 0.02-0.68) and 0.15 ± 0.08 (range: -0.01 to 0.33) for the second measurement, respectively. The difference between the 2 measurements was not statistically significant, and the Bland-Altman plot showed 7.4% and 5.9% points outside the 95% limits of agreement, indicating an overall excellent reproducibility. Similarly, there is no significant difference between the first and second measurements of MPOD volume within eccentricities of 1°, 2°, and 6° radius, and the Bland-Altman plot showed 8.8%, 2.9%, and 4.4% points outside the 95% limits of agreement, respectively. The data for the reproducibility did not differ significantly among the various disease and normal eyes. CONCLUSION: Under routine examination conditions with pupil dilation, MPOD measurement by two-wavelength autofluorescence method showed a high reproducibility.

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

PURPOSE: This 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. METHODS: Images 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. RESULTS: A 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). CONCLUSION: Peripheral 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.

Accuracy and Time Comparison Between Side-by-Side and Artificial Intelligence Overlayed Images.

BACKGROUND AND OBJECTIVE: The purpose of this study was to evaluate the accuracy and the time to find a lesion, taken in different platforms, color fundus photographs and infrared scanning laser ophthalmoscope images, using the traditional side-by-side (SBS) colocalization technique to an artificial intelligence (AI)-assisted technique. PATIENTS AND METHODS: Fifty-three pathological lesions were studied in 11 eyes. Images were aligned using SBS and AI overlaid methods. The location of each color fundus lesion on the corresponding infrared scanning laser ophthalmoscope image was analyzed twice, one time for each method, on different days, for two specialists, in random order. The outcomes for each method were measured and recorded by an independent observer. RESULTS: The colocalization AI method was superior to the conventional in accuracy and time (P < .001), with a mean time to colocalize 37% faster. The error rate using AI was 0% compared with 18% in SBS measurements. CONCLUSIONS: AI permitted a more accurate and faster colocalization of pathologic lesions than the conventional method. [Ophthalmic Surg Lasers Imaging Retina 2023;54:108-113.].

ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.

The Ultra-Wide-Field (UWF) retina images have attracted wide attentions in recent years in the study of retina. However, accurate registration between the UWF images and the other types of retina images could be challenging due to the distortion in the peripheral areas of an UWF image, which a 2D warping can not handle. In this paper, we propose a novel 3D distortion correction method which sets up a 3D projection model and optimizes a dense 3D retina mesh to correct the distortion in the UWF image. The corrected UWF image can then be accurately aligned to the target image using 2D alignment methods. The experimental results show that our proposed method outperforms the state-of-the-art method by 30%.

Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.

Objective: To develop automated algorithms for the detection of posterior vitreous detachment (PVD) using OCT imaging. Design: Evaluation of a diagnostic test or technology. Subjects: Overall, 42 385 consecutive OCT images (865 volumetric OCT scans) obtained with Heidelberg Spectralis from 865 eyes from 464 patients at an academic retina clinic between October 2020 and December 2021 were retrospectively reviewed. Methods: We developed a customized computer vision algorithm based on image filtering and edge detection to detect the posterior vitreous cortex for the determination of PVD status. A second deep learning (DL) image classification model based on convolutional neural networks and ResNet-50 architecture was also trained to identify PVD status from OCT images. The training dataset consisted of 674 OCT volume scans (33 026 OCT images), while the validation testing set consisted of 73 OCT volume scans (3577 OCT images). Overall, 118 OCT volume scans (5782 OCT images) were used as a separate external testing dataset. Main Outcome Measures: Accuracy, sensitivity, specificity, F1-scores, and area under the receiver operator characteristic curves (AUROCs) were measured to assess the performance of the automated algorithms. Results: Both the customized computer vision algorithm and DL model results were largely in agreement with the PVD status labeled by trained graders. The DL approach achieved an accuracy of 90.7% and an F1-score of 0.932 with a sensitivity of 100% and a specificity of 74.5% for PVD detection from an OCT volume scan. The AUROC was 89% at the image level and 96% at the volume level for the DL model. The customized computer vision algorithm attained an accuracy of 89.5% and an F1-score of 0.912 with a sensitivity of 91.9% and a specificity of 86.1% on the same task. Conclusions: Both the computer vision algorithm and the DL model applied on OCT imaging enabled reliable detection of PVD status, demonstrating the potential for OCT-based automated PVD status classification to assist with vitreoretinal surgical planning. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume.

Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.

Effect of change in drusen evolution on photoreceptor inner segment/outer segment junction.

PURPOSE: To evaluate the integrity of photoreceptor inner segment/outer segment (IS/OS) junction after change of drusen size in age-related macular degeneration using spectral-domain optical coherence tomography. METHODS: Drusen volume raster scans were performed with the Spectralis spectral-domain optical coherence tomography (Heidelberg Engineering) through 2,624 drusen in 14 eyes with clinically dry age-related macular degeneration, which had been longitudinally followed-up between 23 and 28 months without intervention (mean, 26.3 months). All eyes had Early Treatment Diabetic Retinopathy Study visual acuity. A total of 416 of 2,624 drusen were analyzed. RESULTS: Of 416 drusen, 83 (20%) were found to have regressed spontaneously (Group A), 212 (51%) showed no change in size (Group B), and 121 (29%) progressed (Group C). Mean drusen size of all drusen was 63.7 ± 25.7 µm. Cross-sectional analysis of drusen morphology showed a correlation between drusen size and disrupted IS/OS junction/photoreceptor integrity (r = -0.48, P < 0.001). Of the drusen that regressed over time, there was intact IS/OS junction integrity. Even drusen that caused a major disruption showed IS/OS restoration in 74% of the drusen (P < 0.001). CONCLUSION: Progression of drusen shows structural disruption of the IS/OS junction. After drusen regression, the IS/OS junction is either able to restore as drusen regress or was artifactitiously compressed and not initially visible because of the initial drusen compression of the IS/OS junctional line. Therefore, drusen evolution may play an important role in affecting the photoreceptor IS/OS junction integrity.

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.

A Mouse Model with Ablated Asparaginase and Isoaspartyl Peptidase 1 (Asrgl1) Develops Early Onset Retinal Degeneration (RD) Recapitulating the Human Phenotype.

We previously identified a homozygous G178R mutation in human ASRGL1 (hASRGL1) through whole-exome analysis responsible for early onset retinal degeneration (RD) in patients with cone-rod dystrophy. The mutant G178R ASRGL1 expressed in Cos-7 cells showed altered localization, while the mutant ASRGL1 in E. coli lacked the autocatalytic activity needed to generate the active protein. To evaluate the effect of impaired ASRGL1 function on the retina in vivo, we generated a mouse model with c.578_579insAGAAA (NM_001083926.2) mutation (Asrgl1mut/mut) through the CRISPR/Cas9 methodology. The expression of ASGRL1 and its asparaginase activity were undetectable in the retina of Asrgl1mut/mut mice. The ophthalmic evaluation of Asrgl1mut/mut mice showed a significant and progressive decrease in scotopic electroretinographic (ERG) response observed at an early age of 3 months followed by a decrease in photopic response around 5 months compared with age-matched wildtype mice. Immunostaining and RT-PCR analyses with rod and cone cell markers revealed a loss of cone outer segments and a significant decrease in the expression of Rhodopsin, Opn1sw, and Opn1mw at 3 months in Asrgl1mut/mut mice compared with age-matched wildtype mice. Importantly, the retinal phenotype of Asrgl1mut/mut mice is consistent with the phenotype observed in patients harboring the G178R mutation in ASRGL1 confirming a critical role of ASRGL1 in the retina and the contribution of ASRGL1 mutations in retinal degeneration.

Long-term SD-OCT/SLO imaging of neuroretina and retinal pigment epithelium after subthreshold infrared laser treatment of drusen.

PURPOSE: The purpose of this study was to determine the long-term effect of subthreshold diode laser treatment for drusen in patients with nonexudative age-related macular degeneration with spectral domain optical coherence tomography combined with simultaneous scanning laser ophthalmoscope. METHODS: Eight eyes of four consecutive age-related macular degeneration patients with bilateral drusen previously treated with subthreshold diode laser were imaged with spectral domain optical coherence tomography/scanning laser ophthalmoscope. Abnormalities in the outer retinal layers' reflectivity as seen with spectral domain optical coherence tomography/scanning laser ophthalmoscope were retrospectively analyzed and compared with color fundus pictures, and autofluorescence images were acquired immediately before and after the laser treatment. RESULTS: A focal discrete disruption in the reflectivity of the outer retinal layers was noted in 29% of the laser lesions. The junction in between the inner and outer segment of the photoreceptor was more frequently affected, with associated focal damage of the outer nuclear layer. Defects of the retinal pigment epithelium were occasionally detected. These changes did not correspond to threshold burns on color fundus photography but corresponded to focal areas of increased autofluorescence in the majority of the cases. CONCLUSION: Subthreshold diode laser treatment causes long-term disruption of the retinal photoreceptor layer as analyzed by spectral domain optical coherence tomography/scanning laser ophthalmoscope. The concept that subthreshold laser treatment can achieve a selected retinal pigment epithelium effect without damage to rods and cones may be flawed.

Scanning laser ophthalmoscope imaging stabilized microperimetry in dry age-related macular degeneration.

PURPOSE: To determine the effect of drusen and geographic atrophy (GA) in dry age-related macular degeneration on retinal sensitivity using an eye tracking scanning laser ophthalmoscope microperimetry. METHODS: A total of 44 eyes from 22 patients with dry age-related macular degeneration and drusen and 11 patients with GA were imaged with scanning laser ophthalmoscope microperimetry (OPKO Health, Miami, FL). A custom microperimetry pattern was used to evaluate retinal sensitivity to a Goldmann III size target (108 µm on the retina). The perimetry used a 4-2 stepladder algorithm to determine maximal sensitivity. Microperimetry and optical coherence tomography were performed using a standardized protocol. Twenty-eight eyes with drusen and 16 eyes with GA were analyzed. RESULTS: Retinal sensitivity overlying drusen was significantly reduced compared with the adjacent uninvolved retina. There was a significant correlation between retinal sensitivity and drusen volume, as well as the grading of the photoreceptor inner segment/outer segment junction score. In patients with GA, an absolute scotoma was confirmed. Retinal sensitivity at the margin of GA was significantly decreased compared with the adjacent uninvolved retina. CONCLUSION: Scanning laser ophthalmoscope microperimetry is able to detect changes in retinal sensitivity in AMD patients overlying drusen and at the margin of GA. It is a useful device to grade focal retinal sensitivity in patients with dry age-related macular degeneration.

LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING.

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

Assessing the validity of a cross-platform retinal image segmentation tool in normal and diseased retina.

Comparing automated retinal layer segmentation using proprietary software (Heidelberg Spectralis HRA + OCT) and cross-platform Optical Coherence Tomography (OCT) segmentation software (Orion). Image segmentations of normal and diseased (iAMD, DME) eyes were performed using both softwares and then compared to the 'gold standard' of manual segmentation. A qualitative assessment and quantitative (layer volume) comparison of segmentations were performed. Segmented images from the two softwares were graded by two masked graders and in cases with difference, a senior retina specialist made a final independent decisive grading. Cross-platform software was significantly better than the proprietary software in the segmentation of NFL and INL layers in Normal eyes. It generated significantly better segmentation only for NFL in iAMD and for INL and OPL layers in DME eyes. In normal eyes, all retinal layer volumes calculated by the two softwares were moderate-strongly correlated except OUTLY. In iAMD eyes, GCIPL, INL, ONL, INLY, TRV layer volumes were moderate-strongly correlated between softwares. In eyes with DME, all layer volume values were moderate-strongly correlated between softwares. Cross-platform software can be used reliably in research settings to study the retinal layers as it compares well against manual segmentation and the commonly used proprietary software for both normal and diseased eyes.

Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.

Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.

Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.

Dynamics of the macular hole-silicone oil tamponade interface with patient positioning as imaged by spectral domain-optical coherence tomography.

PURPOSE: The purposes of this study were to evaluate with spectral domain-optical coherence tomography the relationship between the retina and overlying silicone oil tamponade after macular hole surgery and to evaluate how this relationship changes with patient positioning. METHODS: We studied a retrospective consecutive case series of 10 eyes from 9 patients who underwent macular hole surgery with silicone oil tamponade and subsequent spectral domain-optical coherence tomography scans. Four of the included eyes were also imaged with patients in face-down posture to determine whether the silicone-retina apposition changes with prone positioning. Finally, a single patient was also scanned in the lateral and supine positions. RESULTS: The posterior surface of the silicone oil bubble was well visualized in all 10 eyes. In the majority of eyes (7 of 10), the oil tamponade bridged the macular hole, creating a prefoveal fluid space, but in 3 eyes the silicone oil filled the macular hole and was seen in touch with the underlying foveal depression or retinal pigment epithelium. In 75% of eyes (3 of 4), the silicone oil-retinal approximation did not vary with face-down position. Supine positioning clearly floated the silicone tamponade anteriorly and off the retinal surface. CONCLUSION: Silicone oil tamponade can either bridge macular holes or, in a novel finding, fill the underlying foveal depression or macular hole space. Generally, the oil position is stable between face-forward and prone spectral-domain optical coherence tomography images, suggesting that either of these patient positions allows waterproofing of the underlying macular hole. Finally, our images confirm that supine positioning should be avoided postoperatively because it leads to loss of oil-retinal tamponade.

Spectral domain optical coherence tomography for imaging ERM, retinal edema, and vitreomacular interface.

PURPOSE: To evaluate images taken with OTI-OPKO Spectral domain optical coherence tomography (OCT; OTI-OPKO Health Inc, Miami, FL)/scanning laser ophthalmoscope (resolution of 5-8 microm) and compare them with conventional StratusOCT (Carl Zeiss Meditec Inc, Dublin, CA) in eyes with epiretinal membranes (ERMs), macular edema, and vitreoretinal interface abnormalities. METHODS: We evaluated 79 consecutive eyes with retinal pathologies using Spectral OCT/scanning laser ophthalmoscope and StratusOCT at the Jacobs Retina Center, University of California San Diego, CA. Pathologies included ERM, macular edema, and vitreomacular traction. Two masked reviewers graded the pathologic findings on the basis of visibility (scale 0-III). A quantitative continuous scale grading system was also used. RESULTS: Statistical analysis showed significant differences in ERM visibility between the spectral OCT/scanning laser ophthalmoscope and StratusOCT (P < 0.0001; signed-rank test). Furthermore, posterior hyaloid visibility was significantly different (P < 0.0001) as was the macular edema grading (P < 0.0001). The Gaussian noise grading system was performed for a smaller subset of 40 eyes and it gave the same results. Spectral OCT was particularly useful in the diagnosis of subtle ERM, minimal diffuse macular edema, and morphology of macular cystic spaces, posterior vitreous detachment, and attachments of the posterior hyaloid. CONCLUSION: The Spectral OTI-OPKO instrument allows significantly better visualization of vitreoretinal surface diseases like ERM, posterior hyaloid, and retinal edema than StratusOCT. High-speed Spectral OCT/scanning laser ophthalmoscope allows rapid image acquisition, higher number of cuts, and better sampling yielding superior imaging of retinal pathology.