Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization

Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence

Purpose

To predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.

Materials and methods

Study eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.

Results

Data of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.

Conclusion

The proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD.

Long-term Retinal Morphology and Functional Associations in Treated Neovascular Age-Related Macular Degeneration: Findings from the Inhibition of VEGF in Age-Related Choroidal Neovascularisation Trial

PURPOSE

To describe the frequency of long-term morphologic features and their relationships with visual function in participants who exited the Inhibition of VEGF in Age-Related Choroidal Neovascularisation (IVAN; ISRCTN92166560) trial.

DESIGN

Multicenter cohort study up to 7 years after enrollment.

PARTICIPANTS

Patients enrolled in the IVAN trial, excluding participants who died or withdrew during the trial.

METHODS

Multimodal fundus images, best-corrected visual acuity (BCVA), and low-luminance visual acuity (LLVA) were obtained for a subset of 199 participants who attended a research visit. Clinical sites (n = 20) also provided all visual acuity and clinical information from usual care records for 532 participants and submitted the most recent color, OCT, and other fundus images for 468 participants to a reading center.

MAIN OUTCOME MEASURES

Assessed the following from the most recent images: intralesional macular atrophy (ILMA) within the footprint of the neovascular lesion; hyperreflective material (HRM); intraretinal fluid (IRF); subretinal fluid (SRF); pigment epithelial detachment (PED); and disorganized retinal outer layers (DROLs). Cross-sectional relationships between morphologic features and BCVA/LLVA were estimated.

RESULTS

Intralesional macular atrophy was present in 31.8% of the study eyes at IVAN exit (mean follow-up, 1.96 years) and 89.5% at the most recent imaging visit (mean follow-up, 6.18 years). Hyperreflective material, IRF, SRF, PED, and DROLs were present in 78.8%, 47.7%, 7.6%, 94.5%, and 55% of the study eyes, respectively. In the subset with complete imaging data, in eyes without DROL, the BCVA was worst in the thinnest outer fovea tertile (thinnest minus middle and thickest tertiles, -19.7 and -19.5 letters, respectively), whereas in eyes with DROL, the BCVA was worst in the thickest (thinnest and middle tertiles minus thickest, 12.5 and 12.2, respectively). Regression models showed that the presence of ILMA and HRM was independently associated with BCVA (22 letters worse [95% confidence interval {CI}, -11.2 to -32.8; P < 0.001] and 9.8 letters worse [95% CI, -0.1 to -19.4; P = 0.047], respectively). Subretinal fluid and foveal PED were associated with better BCVA (5.9 letters [95% CI, -7.9 to 19.7; P = 0.399] and 6.4 letters [95% CI, -1.1 to 14.0; P = 0.094], respectively). The model with LLVA was similar. A sensitivity analysis involving the entire eligible cohort yielded similar estimates.

CONCLUSIONS

Macular atrophy and HRM were common after 7 years of follow-up and strongly associated with visual outcomes.

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.

MORPHOLOGICAL AND FUNCTIONAL CHARACTERISTICS AT THE ONSET OF EXUDATIVE CONVERSION IN AGE-RELATED MACULAR DEGENERATION

PURPOSE

To characterize retinal morphology differences among different types of choroidal neovascularization and visual function changes at the onset of exudative age-related macular degeneration.

METHODS

In a post hoc analysis of a prospective clinical study, 1,097 fellow eyes from subjects with choroidal neovascularization in the study eye enrolled in the HARBOR trial were evaluated. The onset of exudation was diagnosed on monthly optical coherence tomography by two masked graders. At conversion as well as 1 month earlier, pigment epithelial detachment, intraretinal cystoid fluid, subretinal fluid, subretinal hyperreflective material, as well as ellipsoid zone and external limiting membrane loss were quantitatively analyzed. Hyperreflective foci, retinal pigment epithelial defects, haze and vitreoretinal interface status were evaluated qualitatively. Main outcome measures included visual acuity and rates of morphologic features at conversion and 1 month earlier.

RESULTS

New-onset exudation was detected in 92 eyes. One month before conversion, hyperreflective foci, pigment epithelial detachment, retinal pigment epithelial defects, and haze were present in the majority of eyes. At the onset of exudation, the volumes of intraretinal cystoid fluid, subretinal fluid, subretinal hyperreflective material and pigment epithelial detachment, and the areas of external limiting membrane and ellipsoid zone loss significantly increased. The mean vision loss was -2.2 letters. Pathognomonic patterns of the different choroidal neovascularization types were already apparent 1 month before conversion.

CONCLUSION

Characteristic choroidal neovascularization-associated morphological changes are preceding disease conversion, while vision loss at the onset of exudation is minimal. Individual lesion types are related to specific changes in optical coherence tomography morphology already before the time of conversion. Our findings may help to elucidate the pathophysiology of neovascular age-related macular degeneration and support the diagnosis of imminent disease conversion.

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

Progression of Anterograde Trans-Synaptic Degeneration in the Human Retina Is Modulated by Axonal Convergence and Divergence

In the visual pathway of patients with multiple sclerosis (MS), the inner nuclear layer (INL) of the retina is a tight barrier for retrograde trans-synaptic degeneration. In this observational, retrospective cross-sectional study, segmented macular spectral domain optical coherence tomography (OCT) volume scans were reviewed to investigate if this observation also holds true for anterograde trans-synaptic degeneration. Significant thinning was found in all retinal layers in patients with outer retinal diseases compared with the healthy controls, while there was no significant attenuation of the outer retina in patients with MS. In contrast to the tight barrier function observed with retrograde trans-synaptic degeneration, the INL appears to be more permissive for the propagation of anterograde trans-synaptic degeneration. We speculate that this may be due to the size of the area affected and be explained by convergence and divergence of axons within the retinal layers. These findings are likely relevant to future restorative stem cell treatment of the outer retinal layers, as time may matter.

Artificial intelligence in retina

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.

Benefits of aflibercept treatment for age-related macular degeneration patients with good best-corrected visual acuity at baseline

Currently, age-related macular degeneration (AMD) is treated while patients exhibit good best-corrected visual acuity (BCVA). However, previous clinical trials only include patients with poor BCVA. We prospectively analyzed the benefits of intravitreal aflibercept (IVA) treatment for AMD patients exhibiting good BCVA at baseline. Twenty-nine treatment-naive AMD patients (29 eyes) with BCVA better than 0.6 (74 letters in ETDRS chart) were treated with IVA once a month for 3 months and every 2 months thereafter with no additional treatments. Improvement in mean BCVA, measured using the conventional Landolt C chart, contrast VA chart, and functional VA (FVA) system, and reductions in mean central retinal thickness (CRT), central choroidal thickness, macular volume (MV), and choroidal area on optical coherence tomography images were observed at 6 and 12 months. Improvements in contrast VA and FVA scores, in contrast to conventional BCVA, correlated with MV reduction; no VA scores correlated with a reduced CRT. The MV correlated with choroidal area after IVA. No severe adverse events occurred. IVA improved visual function, retinal condition, and quality of life evaluated by Visual Function Questionnaire, and was beneficial in these patients. The contrast VA and FVA scores and MVs, which detect subtle changes, helped demonstrate the benefits.

Novel method using 3-dimensional segmentation in spectral domain-optical coherence tomography imaging in the chick reveals defocus-induced regional and time-sensitive asymmetries in the choroidal thickness

Studies into the mechanisms underlying the active emmetropization process by which neonatal refractive errors are corrected, have described rapid, compensatory changes in the thickness of the choroidal layer in response to imposed optical defocus. While high frequency A-scan ultrasonography, as traditionally used to characterize such changes, offers good resolution of central (on-axis) changes, evidence of local retinal control mechanisms make it imperative that more peripheral, off-axis changes also be tracked. In this study, we used in vivo high resolution spectral domain-optical coherence tomography (SD-OCT) imaging in combination with the Iowa Reference Algorithms for 3-dimensional segmentation, to more fully characterize these changes, both spatially and temporally, in young, 7-day old chicks (n = 15), which were fitted with monocular +15 D defocusing lenses to induce choroidal thickening. With these tools, we were also able to localize the retinal area centralis, which was used as a landmark along with the ocular pectin in standardizing the location of scans and aligning them for subsequent analyses of choroidal thickness (CT) changes across time and between eyes. Values were derived for each of four quadrants, centered on the area centralis, and global CT values were also derived for all eyes. Data were compared with on-axis changes measured using ultrasonography. There were significant on-axis choroidal thickening that was detected after just one day of lens wear (∼190 µm), and regional (quadrant-related) differences in choroidal responses were also found, as well as global thickness changes 1 day after treatment. The ratio of global to on-axis choroidal thicknesses, used as an index of regional variability in responses, was also found to change significantly, reflecting the significant central changes. In summary, we demonstrated in vivo high resolution SD-OCT imaging, used in combination with segmentation algorithms, to be a viable and informative approach for characterizing regional (spatial), time-sensitive changes in CT in small animals such as the chick.

Automated segmentation of the choroid in EDI-OCT images with retinal pathology using convolution neural networks

The choroid plays a critical role in maintaining the portions of the eye responsible for vision. Specific alterations in the choroid have been associated with several disease states, including age-related macular degeneration (AMD), central serous choroiretinopathy, retinitis pigmentosa and diabetes. In addition, choroid thickness measures have been shown as a predictive biomarker for treatment response and visual function. Where several approaches currently exist for segmenting the choroid in optical coherence tomography (OCT) images of healthy retina, very few are capable of addressing images with retinal pathology. The difficulty is due to existing methods relying on first detecting the retinal boundaries before performing the choroidal segmentation. Performance suffers when these boundaries are disrupted or suffer large morphological changes due to disease, and cannot be found accurately. In this work, we show that a learning based approach using convolutional neural networks can allow for the detection and segmentation of the choroid without the prerequisite delineation of the retinal layers. This avoids the need to model and delineate unpredictable pathological changes in the retina due to disease. Experimental validation was performed using 62 manually delineated choroid segmentations of retinal enhanced depth OCT images from patients with AMD. Our results show segmentation accuracy that surpasses those reported by state of the art approaches on healthy retinal images, and overall high values in images with pathology, which are difficult to address by existing methods without pathology specific heuristics.

A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration

Optical coherence tomography (OCT) has become an established diagnostic technology in the clinical management of age-related macular degeneration (AMD). OCT is being used for primary diagnosis, evaluation of therapeutic efficacy, and long-term monitoring. Computer-based advances in image analysis provide complementary imaging tools such as OCT angiography, further novel automated analysis methods as well as feature detection and prediction of prognosis in disease and therapy by machine learning. In early AMD, pathognomonic features such as drusen, pseudodrusen, and abnormalities of the retinal pigment epithelium (RPE) can be imaged in a qualitative and quantitative way to identify early signs of disease activity and define the risk of progression. In advanced AMD, disease activity can be monitored clearly by qualitative and quantified analyses of fluid pooling, such as intraretinal cystoid fluid, subretinal fluid, and pigment epithelial detachment (PED). Moreover, machine learning methods detect a large spectrum of new biomarkers. Evaluation of treatment efficacy and definition of optimal therapeutic regimens are an important aim in managing neovascular AMD. In atrophic AMD hallmarked by geographic atrophy (GA), advanced spectral domain (SD)-OCT imaging largely replaces conventional fundus autofluorescence (FAF) as it adds insight into the condition of the neurosensory layers and associated alterations at the level of the RPE and choroid. Exploration of imaging features by computerized methods has just begun but has already opened relevant and reliable horizons for the optimal use of OCT imaging for individualized and population-based management of AMD-the leading retinal epidemic of modern times.

GANGLION CELL LAYER THICKNESS AND VISUAL IMPROVEMENT AFTER EPIRETINAL MEMBRANE SURGERY

PURPOSE

To evaluate the possible prognostic value of preoperative individual retinal layer thicknesses measured by an automated algorithm from spectral domain optical coherence tomography and visual acuity or improvement after epiretinal membrane surgery.

METHODS

Data from 76 eyes with idiopathic epiretinal membrane that underwent pars plana vitrectomy for idiopathic epiretinal membrane removal were analyzed. The preoperative thicknesses of the ganglion cell layer, inner plexiform layer, and other layers were measured using the Iowa Reference Algorithm. Each retinal layer thickness and its ratio of the central foveal thickness were compared between eyes with (Group 1) or without (Group 2) 2 or more Snellen lines of visual improvement at 3, 6, and 12 months after surgery.

RESULTS

Higher mean central foveal thickness/ganglion cell layer ratio and symptom duration of ≤1 year were significantly more common in Group 1 (P = 0.03 and 0.04, respectively). After adjusting for age and symptom duration, lens status, and preoperative visual acuity, higher central foveal thickness/ganglion cell layer ratio was associated with ≥2 lines of visual improvement after surgery (odds ratio: 6.57, 95% confidence interval: 1.29-33.40).

CONCLUSION

The preoperative inner retinal layer changes may have a role independent of outer retinal layer parameters in the visual prognosis after epiretinal membrane peeling.