Morphological and Functional Correlations in Acute Central Serous Chorioretinopathy.

PURPOSE: We evaluate morphological and functional correlations in patients with acute central serous chorioretinopathy (CSC). METHODS: A prospective study was conducted on 50 patients with an acute CSC episode lasting less than 3 months. At baseline, assessments included optical coherence tomography (OCT), best-corrected visual acuity (BCVA), contrast sensitivity (CS), microperimetry (MP), and multifocal electroretinography (mfERG). A correlation analysis between OCT morphological parameters (maximal subretinal fluid height (SRF), central retinal thickness (CRT), and macular volume (MV)) and functional parameters was conducted on the affected eye for each patient. RESULTS: Among the morphological parameters, SRF showed the strongest correlations with functional parameters (r absolute value range = 0.10-0.70). Weak correlations were observed between BCVA and morphological parameters (r absolute value range = 0.14-0.26). Average retinal sensitivity (MP-A) was the functional parameter displaying the most robust negative correlation with morphological parameters (r absolute value range = 0.61-0.70). In contrast, average contrast sensitivity (CS-A) and mfERG average amplitude density in the first (mfERG-A1) and second (mfERG-A2) ring showed weak to moderate (r absolute value range = 0.35-0.56) yet statistically significantly nonzero correlations. CONCLUSIONS: SRF and CRT could serve as the most representative morphological proxies for visual function deficit in acute CSC patients. Retinal sensitivity, as measured by MP, may be superior to BCVA in clinical research studies or when an in-depth visual function evaluation is needed.

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values. METHODS: We utilized an automated deep learning approach to quantify macular fluid in SD-OCT volumes (Cirrus, Spectralis, Topcon) from patients of HAWK and HARRIER Studies. Three-dimensional volumes for IRF and SRF were measured at baseline and under therapy in the central millimeter and compared to fluid gradings, CSFT and foveal centerpoint thickness (CPT) values measured by the Vienna Reading Center. RESULTS: 41.906 SD-OCT volume scans were included into the analysis. Concordance between human expert grading and automated algorithm performance reached AUC values of 0.93/0.85 for IRF and 0.87 for SRF in HARRIER/HAWK in the central millimeter. IRF volumes showed a moderate correlation with CSFT at baseline (HAWK: r = 0.54; HARRIER: r = 0.62) and weaker correlation under therapy (HAWK: r = 0.44; HARRIER: r = 0.34). SRF and CSFT correlations were low at baseline (HAWK: r = 0.29; HARRIER: r = 0.22) and under therapy (HAWK: r = 0.38; HARRIER: r = 0.45). The residual standard error (IRF: 75.90 µm; SRF: 95.26 µm) and marginal residual standard deviations (IRF: 46.35 µm; SRF: 44.19 µm) of fluid volume were high compared to the range of CSFT values. CONCLUSION: Deep learning-based segmentation of retinal fluid performs reliably on OCT images. CSFT values are weak indicators for fluid activity in nAMD. Automated quantification of fluid types, highlight the potential of deep learning-based approaches to objectively monitor anti-VEGF therapy.

Does Subretinal Fluid Optical Density Ratio Differ Among the Eyes with Acute Central Serous Chorioretinopathy,Vogt Koyanagi Harada Disease and Choroidal Hemangioma: A Cross-sectional Study.

PURPOSE: To investigate the diagnostic value of optical density ratio (ODR) in various diseases with subretinal fluid (SRF) due to different pathophysiologies. METHODS: Patients with acute central serous chorioretinopathy, CSCR (n = 49), Vogt Koyanagi Harada disease, VKH (n = 34), and choroidal hemangioma (n = 17) characterized with SRF were included. Spectral-domain optical coherence tomography (SD-OCT) images were analyzed using ImageJ by three independent readers. The ODRs were calculated using "region of interest (ROI)" and "entire region (TOTAL)" selection methods from the SRF to the vitreous, retinal nerve fiber layer (RNFL), and retinal pigment epithelium (RPE) reflectivity ratios. A correlation analysis between age, central macular thickness (CMT), SRF height, SRF width, and ODRs were obtained. RESULTS: Optical density (OD) measurement was highly reproducible (intraclass correlation coefficient> 0.9). Optical density of the SRF, vitreous, RNFL, and signal strength were comparable (p = 0.360, p = 0.247, p = 0.105, and 0.628, respectively). There was no difference in SRF OD measurements between the two methods (p = 0.401), while there was a significant difference in vitreous OD measurements (p = 0.016). ANOVA test of ODR(ROI), ODR(TOTAL), ODR-RPE (ROI) and ODR-RNFL (ROI) revealed no significant difference among acute CSCR, VKH disease and choroidal hemangioma groups (p > 0.05 for all). Correlation analysis revealed a significant negative correlation between SRF height (p < 0.05) and CMT (p < 0.01) with SRF ODR(ROI). CONCLUSION: ODR measurement appears to be a highly repeatable SD-OCT parameter for diseases characterized with SRF collection. Despite variations in their pathophysiology, the ODR was not statistically different in acute CSCR, VKH disease, and choroidal hemangioma.

ADVANTAGES OF OPTICAL COHERENCE TOMOGRAPHY AS A HIGH DYNAMIC RANGE IMAGING MODALITY IN SUBRETINAL HYPERREFLECTIVE MATERIAL.

PURPOSE: To describe the utility of high dynamic range optical coherence tomography imaging to study subretinal hyperreflective material (SHRM) in patients with age-related macular degeneration. METHODS: Clinical information including visual acuity and optical coherence tomography images (Heidelberg Engineering GmbH, Heidelberg, Germany) of patients undergoing antiangiogenic treatment for neovascular age-related macular degeneration and showing SHRM at baseline were retrospectively reviewed. Contrast between strong signal structures (high dynamic range image) reclassifying SHRM as hyperreflective (HyperR), isoreflective, and hyporeflective was increased. The patients at baseline, 3, 6, and 12-months follow-up were evaluated. RESULTS: Forty-four eyes were classified as 15 HyperR (34.1%), 21 as isoreflective (47.7%), and eight as hyporeflective (18.2%). During follow-up, hyporeflective SHRM disappeared in all cases, isoreflective SHRM faded in 16 cases (76.2%); HyperR SHRM remained in all cases. Hyporreflective SHRM showed a greater visual acuity improvement than HyperR SHRM group ( P = 0.033). After 12-month follow-up, only the hyporeflective and isoreflective groups significantly reduced the presence of fluid in 37.5% ( P = 0.250) and 46.62% ( P = 0.006) of the patients, respectively; outer retinal layers were disrupted more frequently in the presence of HyperR SHRM (ellipsoid zone, P = 0.16; external limiting membrane, P = 0.007). CONCLUSION: Contrast-enhanced optical coherence tomography images enabled us to classify SHRM according to its reflectivity, showing groups with different disappearance rates, visual acuity improvement, and outer retinal layer disruption. This easy-to-access tool may be helpful as a prognostic factor in neovascular age-related macular degeneration cases.

Automatic segmentation of multitype retinal fluid from optical coherence tomography images using semisupervised deep learning network.

BACKGROUND/AIMS: To develop and validate a deep learning model for automated segmentation of multitype retinal fluid using optical coherence tomography (OCT) images. METHODS: We retrospectively collected a total of 2814 completely anonymised OCT images with subretinal fluid (SRF) and intraretinal fluid (IRF) from 141 patients between July 2018 and June 2020, constituting our in-house retinal OCT dataset. On this dataset, we developed a novel semisupervised retinal fluid segmentation deep network (Ref-Net) to automatically identify SRF and IRF in a coarse-to-refine fashion. We performed quantitative and qualitative analyses on the model's performance while verifying its generalisation ability by using our in-house retinal OCT dataset for training and an unseen Kermany dataset for testing. We also determined the importance of major components in the semisupervised Ref-Net through extensive ablation. The main outcome measures were Dice similarity coefficient (Dice), sensitivity (Sen), specificity (Spe) and mean absolute error (MAE). RESULTS: Our model trained on a handful of labelled OCT images manifested higher performance (Dice: 81.2%, Sen: 87.3%, Spe: 98.8% and MAE: 1.1% for SRF; Dice: 78.0%, Sen: 83.6%, Spe: 99.3% and MAE: 0.5% for IRF) over most cutting-edge segmentation models. It obtained expert-level performance with only 80 labelled OCT images and even exceeded two out of three ophthalmologists with 160 labelled OCT images. Its satisfactory generalisation capability across an unseen dataset was also demonstrated. CONCLUSION: The semisupervised Ref-Net required only la few labelled OCT images to generate outstanding performance in automate segmentation of multitype retinal fluid, which has the potential for providing assistance for clinicians in the management of ocular disease.

Does choroidal thickness predict persistent subretinal fluid after rhegmatogenous retinal detachment repair? A retrospective study with fellow eye comparison.

PURPOSE: To evaluate whether choroidal thickness (CT) is associated with persistent subretinal fluid (pSRF) after simple primary rhegmatogenous retinal detachment (RRD) repair. METHODS: This single-centre, retrospective, observational study included patients who underwent RRD repair with at least 12-month follow-up. Preoperative and postoperative parameters were evaluated for association with pSRF. CT measurements were obtained at the central 1 mm area on enhanced depth imaging (EDI) OCT scans, using a semiautomatic method. Multiple logistic regression analyses were assessed to determine predictive factors for pSRF. RESULTS: Overall, 100 eyes of 100 patients, mean age of 59.9 ± 12.6 years were included. pSRF was found in 21.0% of eyes and resolved over time in 85.7% of eyes at 12 months. In the pSRF group both RRD and fellow eyes showed lower mean choroidal and RPE thickness values as compared to those without pSRF (p < 0.05). A significant correlation was found between pSRF occurrence and choroidal thinning (p = 0.02). After multiple regression analyses, macula-off RRD (p = 0.005) and scleral buckling (SB) technique (p = 0.001) were retained as final predictors for pSRF. In macula-off SB eyes, detachment duration was the only factor associated with pSRF (p = 0.046). There were no significant differences in best-corrected visual acuity outcomes between the pSRF and the no-pSRF eyes. CONCLUSIONS: Patients with pSRF showed lower choroidal and RPE thickness as compared to those without pSRF. CT did not turn out to be a final predictor for pSRF, as this was mainly associated with macular involvement, surgical technique and detachment duration.

Clear subretinal fluid in a case of non-neovascular early-onset drusen: Swept-source imaging evaluation.

A 50-year-old male presented with recent metamorphopsia in the right eye. Fundus examination revealed bilateral multiple cuticular drusen along with few large colloid drusen (phenotype 3 cuticular drusen). No vitelliform material was evident in the macula in either eye. Fluorescein angiography (FA), indocyanine green angiography (ICGA) did not demonstrate a macular neovascularization (MNV) in either eye. Swept-source optical coherence tomography (SS-OCT) revealed sub-retinal pigment epithelial (RPE) drusen and a clear space beneath the inter-digitation zone and above the RPE-Bruch's complex. SS-OCTA did not reveal MNV in either eye. The patient was kept under observation, and follow-up at 3 months did not reveal any structural change. Among patients with early-onset drusen, vitelliform detachments may occur due to accumulation of yellow pseudo-vitelliform material. However, serous detachments with an "optically clear" subretinal space can occur by the same mechanism without MNV. Such patients do not merit therapy with anti-vascular endothelial growth factor agents.

Subretinal fluid morphology in chronic central serous chorioretinopathy and its relationship to treatment: a retrospective analysis on PLACE trial data.

PURPOSE: To explore subretinal fluid (SRF) morphology in chronic central serous chorioretinopathy (cCSC) after one session of either high-density subthreshold micropulse laser (HSML) treatment or half-dose photodynamic therapy (PDT). METHODS: We retrospectively obtained optical coherence tomography (OCT) scans from a subset of patients from a randomized controlled trial on treatment-naïve eyes with cCSC allocated to either HSML treatment or half-dose PDT. OCT scans were evaluated prior to treatment and 6-8 weeks post-treatment, where we measured maximum SRF height and width, calculated the maximum height-to-maximum width-ratio (maxHWR) and calculated the total SRF volume. RESULTS: Forty-one eyes of 39 cCSC patients were included. SRF morphology ranged from flat to dome-shaped, quantified as maxHWR ranging between 0.02 and 0.12. SRF volume was median 0.373 µl (range: 0.010-4.425 µl) and did not correlate to maxHWR (rho = -0.004, p = 0.982). Half-dose PDT was superior to HSML treatment in complete SRF resolution (RR = 3.28, p = 0.003) and in morphological changes of SRF (Δmaximum height , p = 0.001; Δmaximum width , p < 0.001; Δvolume , p = 0.025). SRF resolved completely in 19/22 PDT-treated eyes (86%) and 5/19 HSML-treated eyes (26%). SRF volume increased in five eyes (26%) after HSML treatment, and in none of the eyes after half-dose PDT. SRF morphology at baseline did not predict treatment outcomes. CONCLUSION: SRF morphology changed after both HSML treatment and half-dose PDT in cCSC, with SRF disappearing in most PDT-treated patients, whereas SRF volume increased in a sizeable proportion of HSML-treated patients. Baseline SRF characteristics measured in this study were unable to predict outcomes after either HSML treatment or half-dose PDT.

AI-based monitoring of retinal fluid in disease activity and under therapy.

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

Intraretinal, sub-retinal, and sub-retinal pigmented epithelium fluid in non-exudative age-related macular degeneration: follow-up with OCT imaging.

BACKGROUND/OBJECTIVES: To evaluate the presence and evolution of fluid in non-exudative age-related macular degeneration (AMD) through serial OCT. SUBJECTS/METHODS: A retrospective analysis of eyes with non-exudative AMD with a minimum of 4 year follow-up was done. Parameters including intraretinal fluid (IRF), subretinal fluid (SRF), and sub-retinal pigment epithelium (RPE) fluid (SRPEF); subfoveal choroidal thickness (SFCT) and type of drusen were evaluated using optical coherence tomography (OCT) scans at baseline and follow up visits. RESULTS: Seventy-two eyes (in 63 patients) were followed up for an average of 5.83 ± 2.17 years. A total of 26/72 (36%) and 29/65 (52%) of the non-exudative eyes had fluid during baseline and the last visit. Seven eyes (10%) out of 72 eyes converted into exudative AMD or neo-vascular AMD (nAMD) during the study period. SRPEF at baseline was most common fluid location for non-exudative eyes that eventually converted to nAMD. CONCLUSION: Non-exudative fluid including IRF, SRF, and SRPEF is seen in patients with non-exudative AMD with increasing incidence during long term follow-up.

COMPARATIVE EVALUATION OF OUTCOMES OF DRAINAGE TECHNIQUES IN VITRECTOMY FOR RHEGMATOGENOUS RETINAL DETACHMENT.

PURPOSE: To compare the anatomical and functional outcomes of drainage through posterior retinotomy versus perfluorocarbon liquid (PFCL)-assisted drainage in vitreoretinal surgery for rhegmatogenous retinal detachment and to study intraoperative and postoperative complications. METHODS: This was a prospective randomized study of 52 cases who underwent vitreoretinal surgery for rhegmatogenous retinal detachment. Group 1 underwent PFCL-assisted drainage through preexisting break, whereas Group 2 had posterior retinotomy to drain subretinal fluid. Cases were evaluated for retinal reattachment rates, visual outcomes, optical coherence tomography parameters, and postoperative metamorphopsia. The patients were followed up for minimum period of 3 months. RESULTS: Two groups were comparable in terms of demographic and preoperative parameters. Both groups had single surgery success rate of 100% by the end of follow-up. Final best-corrected visual acuity in Group 1 was 0.61 ± 0.33 and 0.61 ± 0.32 in Group 2 (P = 0.77). Optical coherence tomography parameters (foveal contour, retinal layers, central macular thickness, and epiretinal membrane formation) were similar between the two groups. Subjective metamorphopsia was present in 30.77% (8 of 26) patients in Group-1 and 69.23% (18 of 26) patients in Group-2 (P = 0.034). One eye had retained subretinal PFCL away from the macula in Group 1. CONCLUSION: Anatomical and functional outcomes were similar in vitrectomy using PFCL-assisted drainage versus posterior retinotomy drainage. Postoperative metamorphopsia was lesser in patients who underwent PFCL-assisted drainage through the pre-existing break.

Impact of Intra- and Subretinal Fluid on Vision Based on Volume Quantification in the HARBOR Trial.

PURPOSE: To investigate the functional associations of intraretinal fluid (IRF) and subretinal fluid (SRF) volumes at baseline and after the loading dose as well as fluid change after the first injection with best-corrected visual acuity (BCVA) in patients with neovascular age-related macular degeneration (nAMD) who received an anti-VEGF treatment over 24 months. DESIGN: Post hoc analysis of a phase III, randomized, multicenter trial in which ranibizumab was administered monthly or in a pro re nata regimen (HARBOR). PARTICIPANTS: Study eyes of 1094 treatment-naïve patients with nAMD. METHODS: IRF and SRF volumes were segmented automatically on monthly spectral domain OCT images. Fluid volumes and changes thereof were included as covariates into longitudinal mixed-effects models, which modeled BCVA trajectories. MAIN OUTCOME MEASURES: BCVA estimates corresponding to baseline, follow-up, and persistent IRF/SRF volumes after the loading dose; BCVA estimates of change in fluid volumes after the first injection; and marginal and conditional R2. RESULTS: Analysis of 22 494 volumetric scans revealed that foveal IRF consistently shows a negative correlation with BCVA at baseline and subsequent visits (-3.23 and -4.32 letters/100 nL, respectively). After the first injection, BCVA increased by +2.13 letters/100 nL decrease in foveal IRF. Persistent IRF was associated with lower baseline BCVA and less improvement. Foveal SRF correlated with better BCVA at baseline and subsequent visits (+6.52 and +1.42 letters/100 nL, respectively). After the first injection, SRF decrease was associated with significant vision gain (+5.88 letters/100 nL). Foveal fluid correlated more with BCVA than parafoveal IRF/SRF. CONCLUSIONS: Although IRF consistently correlates with decreased function and recovery throughout therapy, SRF is associated with a more pronounced functional improvement. Moreover, SRF resolution provides increased benefit. Fluid-function correlation represents an essential base for the development of personalized treatment regimens, optimizing functional outcomes, and reducing treatment burden.

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-based retinal fluids segmentation network (RFS-Net) to segment and recognize three MRF lesion manifestations, namely, intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), from multi-vendor optical coherence tomography (OCT) imagery. The proposed image analysis tool will optimize anti-VEGF therapy and contribute to reducing the inter- and intra-observer variability. METHOD: The proposed RFS-Net architecture integrates the atrous spatial pyramid pooling (ASPP), residual, and inception modules in the encoder path to learn better features and conserve more global information for precise segmentation and characterization of MRF lesions. The RFS-Net model is trained and validated using OCT scans from multiple vendors (Topcon, Cirrus, Spectralis), collected from three publicly available datasets. The first dataset consisted of OCT volumes obtained from 112 subjects (a total of 11,334 B-scans) is used for both training and evaluation purposes. Moreover, the remaining two datasets are only used for evaluation purposes to check the trained RFS-Net's generalizability on unseen OCT scans. The two evaluation datasets contain a total of 1572 OCT B-scans from 1255 subjects. The performance of the proposed RFS-Net model is assessed through various evaluation metrics. RESULTS: The proposed RFS-Net model achieved the mean F1 scores of 0.762, 0.796, and 0.805 for segmenting IRF, SRF, and PED. Moreover, with the automated segmentation of the three retinal manifestations, the RFS-Net brings a considerable gain in efficiency compared to the tedious and demanding manual segmentation procedure of the MRF. CONCLUSIONS: Our proposed RFS-Net is a potential diagnostic tool for the automatic segmentation of MRF (IRF, SRF, and PED) lesions. It is expected to strengthen the inter-observer agreement, and standardization of dosimetry is envisaged as a result.

RetFluidNet: Retinal Fluid Segmentation for SD-OCT Images Using Convolutional Neural Network.

Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD.

Predominantly Persistent Subretinal Fluid in the Comparison of Age-Related Macular Degeneration Treatments Trials.

OBJECTIVE: To describe predominantly persistent subretinal fluid (SRF) in eyes receiving ranibizumab or bevacizumab for neovascular age-related macular degeneration and to compare visual acuity (VA) to eyes with nonpersistent SRF. DESIGN: Cohort within randomized clinical trial. PARTICIPANTS: Comparison of Age-related Macular Degeneration Treatments Trials patients assigned to pro re nata treatment. METHODS: Graders evaluated monthly OCT scans for SRF. Predominantly persistent SRF through week 12 was defined as SRF at baseline and weeks 4, 8, and 12. Predominantly persistent SRF through 1 or 2 years was defined as SRF in 80% or more of visits by years 1 or 2, respectively. Linear regression models including baseline predictors of VA and predominantly persistent intraretinal fluid (IRF) were used to evaluate mean differences in vision outcomes. PRIMARY OUTCOME MEASURES: Predominantly persistent SRF through year 1, adjusted VA score and VA change, and foveal SRF thickness. RESULTS: Among 406 eyes with baseline SRF, SRF persisted in 108 eyes (26.6%) through week 12, in 94 eyes (23.2%) through year 1, and in 77 eyes (19.0%) through year 2. Adjusted VA means at year 1 were similar between eyes with predominantly persistent versus non persistent SRF by week 12 (68.1 vs. 70.2 letters; P = 0.18), year 1 (67.6 vs. 70.2 letters; P = 0.11), and year 2 (71.4 vs. 70.9 letters; P = 0.78). Adjusted changes in mean VA at year 1 were similar between eyes with predominantly persistent versus nonpersistent SRF by week 12 (6.3 vs. 7.6 letters; P = 0.38), year 1 (5.5 vs. 7.8 letters; P = 0.14), and year 2 (8.1 vs. 7.7 letters; P = 0.78). Among eyes with predominantly persistent SRF through year 1, foveal SRF was absent in 46 eyes (48.9%), thickness was 1 to 200 µm in 48 eyes (50.0%) and more than 200 µm in 1 eye (1.1%) at year 1. CONCLUSIONS: Eyes with predominantly persistent and nonpersistent SRF through week 12, year 1, or year 2 showed similar VA outcomes after adjustment for baseline covariates and persistent IRF. At the foveal center, predominantly persistent SRF was most commonly absent or present in small quantities.

Evaluation of Automated Multiclass Fluid Segmentation in Optical Coherence Tomography Images Using the Pegasus Fluid Segmentation Algorithms.

Purpose: To evaluate the performance of the Pegasus-OCT (Visulytix Ltd) multiclass automated fluid segmentation algorithms on independent spectral domain optical coherence tomography data sets. Methods: The Pegasus automated fluid segmentation algorithms were applied to three data sets with edematous pathology, comprising 750, 600, and 110 b-scans, respectively. Intraretinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelial detachment (PED) were automatically segmented by Pegasus-OCT for each b-scan where ground truth from data set owners was available. Detection performance was assessed by calculating sensitivities and specificities, while Dice coefficients were used to assess agreement between the segmentation methods. Results: For two data sets, IRF detection yielded promising sensitivities (0.98 and 0.94, respectively) and specificities (1.00 and 0.98) but less consistent agreement with the ground truth (dice coefficients 0.81 and 0.59); likewise, SRF detection showed high sensitivity (0.86 and 0.98) and specificity (0.83 and 0.89) but less consistent agreement (0.59 and 0.78). PED detection on the first data set showed moderate agreement (0.66) with high sensitivity (0.97) and specificity (0.98). IRF detection in a third data set yielded less favorable agreement (0.46-0.57) and sensitivity (0.59-0.68), attributed to image quality and ground truth grader discordance. Conclusions: The Pegasus automated fluid segmentation algorithms were able to detect IRF, SRF, and PED in SD-OCT b-scans acquired across multiple independent data sets. Dice coefficients and sensitivity and specificity values indicate the potential for application to automated detection and monitoring of retinal diseases such as age-related macular degeneration and diabetic macular edema. Translational Relevance: The potential of Pegasus-OCT for automated fluid quantification and differentiation of IRF, SRF, and PED in OCT images has application to both clinical practice and research.

RETINAL FLUID AND THICKNESS AS MEASURES OF DISEASE ACTIVITY IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

PURPOSE: Retinal fluid and thickness are important anatomical features of disease activity in neovascular age-related macular degeneration, as evidenced by clinical trials that have used these features for inclusion criteria, retreatment criteria, and outcome measures of the efficacy of intravitreal injections of anti-vascular endothelial growth factor agents. METHODS: A literature review of anatomical measures of disease activity was conducted. RESULTS: Treatment goals for neovascular age-related macular degeneration include improving/maintaining vision by drying the retina, and several analyses have evaluated the relationship between visual function and anatomy. The change in retinal thickness has been found to correlate with the change in the visual acuity, and variation in retinal thickness may predict visual acuity outcomes. In addition, specific fluid compartments may have different prognostic values. For example, the presence of intraretinal fluid has been associated with poorer visual acuity, whereas the presence of subretinal fluid has been associated with better visual acuity. Retinal fluid and thickness are important for selecting dosing interval durations in clinical trials and clinical practice. CONCLUSION: Retinal thickness and retinal fluid are common anatomical measures of disease activity in neovascular age-related macular degeneration. Further research is required to fully elucidate the relationship between anatomical features and visual outcomes in neovascular age-related macular degeneration.

Capsule Network-based architectures for the segmentation of sub-retinal serous fluid in optical coherence tomography images of central serous chorioretinopathy.

Central serous chorioretinopathy (CSCR) is a chorioretinal disorder of the eye characterized by serous detachment of the neurosensory retina at the posterior pole of the eye. CSCR results from the accumulation of subretinal fluid (SRF) due to idiopathic defects at the level of the retinal pigment epithelial (RPE) that allows serous fluid from the choriocapillaris to diffuse into the subretinal space between RPE and neurosensory retinal layers. This condition is presently investigated by clinicians using invasive angiography or non-invasive optical coherence tomography (OCT) imaging. OCT images provide a representation of the fluid underlying the retina, and in the absence of automated segmentation tools, currently only a qualitative assessment of the same is used to follow the progression of the disease. Automated segmentation of the SRF can prove to be extremely useful for the assessment of progression and for the timely management of CSCR. In this paper, we adopt an existing architecture called SegCaps, which is based on the recently introduced Capsule Networks concept, for the segmentation of SRF from CSCR OCT images. Furthermore, we propose an enhancement to SegCaps, which we have termed as DRIP-Caps, that utilizes the concepts of Dilation, Residual Connections, Inception Blocks, and Capsule Pooling to address the defined problem. The proposed model outperforms the benchmark UNet architecture while reducing the number of trainable parameters by 54.21%. Moreover, it reduces the computation complexity of SegCaps by reducing the number of trainable parameters by 37.85%, with competitive performance. The experiments demonstrate the generalizability of the proposed model, as evidenced by its remarkable performance even with a limited number of training samples. Graphical abstract is mandatory please provide.

IMPACT OF RESIDUAL SUBRETINAL FLUID VOLUMES ON TREATMENT OUTCOMES IN A SUBRETINAL FLUID-TOLERANT TREAT-AND-EXTEND REGIMEN.

PURPOSE: To investigate associations between residual subretinal fluid (rSRF) volumes, quantified using artificial intelligence and treatment outcomes in a subretinal fluid (SRF)-tolerant treat-and-extend (T&E) regimen in neovascular age-related macular degeneration. METHODS: Patients enrolled in the prospective, multicenter FLUID study randomized in an SRF-tolerant T&E regimen were examined by spectral-domain optical coherence tomography and tested for best-corrected visual acuity (BCVA). Intraretinal fluid and SRF volumes were quantified using artificial intelligence tools. In total, 375 visits of 98 patients were divided into subgroups: extended intervals despite rSRF and extended intervals without fluid. Associations between BCVA change, SRF volume, subgroups, and treatment intervals were estimated using linear mixed models. RESULTS: In extended intervals despite rSRF, increased SRF was associated with reduced BCVA at the next visit in the central 1 mm (-0.138 letters per nL; P = 0.014) and 6 mm (-0.024 letters per nL; P = 0.049). A negative association between increased interval and BCVA change was found for rSRF in 1 mm and 6 mm (-0.250 and -0.233 letter per week interval, respectively; both P < 0.001). Extended intervals despite rSRF had significantly higher SRF volumes in the central 6 mm at the following visit (P = 0.002). CONCLUSION: Artificial intelligence-based analysis of extended visits despite rSRF demonstrated increasing SRF volumes associated with BCVA loss at the consecutive visit. This negative association contributes to the understanding of rSRF volumes on treatment outcomes in neovascular age-related macular degeneration.