SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY, FLUORESCEIN ANGIOGRAPHY, AND INDOCYANINE GREEN ANGIOGRAPHY COMPARISONS REVISITED: Using a Novel Deep-Learning-Assisted Approach for Image Registration

Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review

PURPOSE

To evaluate the evidence for alterations of blood flow, vascular and perfusion densities in the choroid, macula, peripapillary region, and the area surrounding the optic nerve head (ONH) in patients with thyroid-associated ophthalmopathy (TAO) based on changes of OCTA parameters.

METHODS

A systematic review of Pubmed, Google Scholar, Scopus, WOS, Cochrane, and Embase databases, including quality assessment of published studies, investigating the alterations of OCTA parameters in TAO patients was conducted. The outcomes of interest comprised changes of perfusion and vascular densities in radial peripapillary capillary (RPC), ONH, superficial and deep retinal layers (SRL and DRL), choriocapillaris (CC) flow, and the extent of the foveal avascular zone (FAZ).

RESULTS

From the total of 1253 articles obtained from the databases, the pool of papers was narrowed down to studies published until March 20th, 2024. Lastly, 42 studies were taken into consideration which contained the data regarding the alterations of OCTA parameters including choriocapillary vascular flow, vascular and perfusion densities of retinal microvasculature, SRL, and DRL, changes in macular all grid sessions, changes of foveal, perifoveal and parafoveal densities, macular whole image vessel density (m-wiVD) and FAZ, in addition to alterations of ONH and RPC whole image vessel densities (onh-wiVD and rpc-wiVD) among TAO patients. The correlation of these parameters with visual field-associated parameters, such as Best-corrected visual acuity (BCVA), Visual field mean defect (VF-MD), axial length (AL), P100 amplitude, and latency, was also evaluated among TAO patients.

CONCLUSION

The application of OCTA has proven helpful in distinguishing active and inactive TAO patients, as well as differentiation of patients with or without DON, indicating the potential promising role of some OCTA measures for early detection of TAO with high sensitivity and specificity in addition to preventing the irreversible outcomes of TAO. OCTA assessments have also been applied to evaluate the effectiveness of TAO treatment approaches, including systemic corticosteroid therapy and surgical decompression.

Deformable registration of multimodal retinal images using a weakly supervised deep learning approach

There are different retinal vascular imaging modalities widely used in clinical practice to diagnose different retinal pathologies. The joint analysis of these multimodal images is of increasing interest since each of them provides common and complementary visual information. However, if we want to facilitate the comparison of two images, obtained with different techniques and containing the same retinal region of interest, it will be necessary to make a previous registration of both images. Here, we present a weakly supervised deep learning methodology for robust deformable registration of multimodal retinal images, which is applied to implement a method for the registration of fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) images. This methodology is strongly inspired by VoxelMorph, a general unsupervised deep learning framework of the state of the art for deformable registration of unimodal medical images. The method was evaluated in a public dataset with 172 pairs of FA and superficial plexus OCTA images. The degree of alignment of the common information (blood vessels) and preservation of the non-common information (image background) in the transformed image were measured using the Dice coefficient (DC) and zero-normalized cross-correlation (ZNCC), respectively. The average values of the mentioned metrics, including the standard deviations, were DC = 0.72  ±  0.10 and ZNCC = 0.82  ±  0.04. The time required to obtain each pair of registered images was 0.12 s. These results outperform rigid and deformable registration methods with which our method was compared.

Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning

PURPOSE

To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age-related macular degeneration (nAMD).

METHODS

In this prospective interventional study, 54 patients with treatment-naïve type 1 or 2 nAMD were included and treated with intravitreal aflibercept. At baseline and month 1, each patient underwent a SD-OCT volume scan and volumetric flow scan using a swept-source OCTA. A deep learning algorithm was used to automatically detect and quantify fluid in OCT scans. Angio Tool, a National Cancer Institute algorithm, was used to skeletonize MNV properties and quantify lesion size (LS), vessel area (VA), vessel density (VD), total number of endpoints (TNE), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL) and mean-e-lacunarity (MEL). Subsequently, linear regression models were used to investigate a correlation between OCTA parameters and fluid quantifications.

RESULTS

The median amount of fluid within the central 6-mm EDTRS ring was 173.7 nl at baseline, consisting of 156.6 nl of subretinal fluid (SRF) and 2.3 nl of intraretinal fluid (IRF). Fluid decreased significantly in all compartments to 1.76 nl (SRF) and 0.64 nl (IRF). The investigated MNV parameters did not change significantly after the first treatment. There was no significant correlation between MNV parameters and relative fluid decrease after anti-VEGF treatment. Baseline fluid correlated statistically significant but weakly with TNE (p = 0.002, R²  = 0.17), SRF with TVL (p = 0.04, R²  = 0.08), VD (p = 0.046, R²  = 0.08), TNE (p = 0.001, R²  = 0.20) and LS (p = 0.033, R²  = 0.09). IRF correlated with VA (p = 0.042, R²  = 0.08).The amount of IRF at month 1 correlated significantly but weakly with VD (p = 0.036, R²  = 0.08), JD (p = 0.019, R²  = 0.10) and MEL (p = 0.005, R²  = 0.14).

CONCLUSION

Macular neovascularization parameters at baseline and month 1 played only a minor role in the exudation process in nAMD. None of the MNV parameters were correlated with the treatment response.

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF. By mapping OCT B-scans onto the accompanying en face infrared (IR) image and then registering the IR image onto the FAF image by a neural network, we can directly compare OCT labels to other labels in the en face plane. We also present a U-Net inspired segmentation model to predict segmentations in unlabeled OCTs. Evaluations show that both our networks achieve high precision (0.853 Dice score and 0.913 Area under Curve). Furthermore, medical analysis performed on exemplary, chronologically arranged CSCR progressions of 12 patients visualized with our pipeline indicates that, on CSCR, two patterns emerge: subretinal fluid (SRF) in OCT preceding hyperfluorescence (HF) in FAF and vice versa.

Pathologic myopia: advances in imaging and the potential role of artificial intelligence

Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.

Robust multimodal registration of fluorescein angiography and optical coherence tomography angiography images using evolutionary algorithms

Optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) are two different vascular imaging modalities widely used in clinical practice to diagnose and grade different relevant retinal pathologies. Although each of them has its advantages and disadvantages, the joint analysis of the images produced by both techniques to analyze a specific area of the retina is of increasing interest, given that they provide common and complementary visual information. However, in order to facilitate this analysis task, a previous registration of the pair of FA and OCTA images is desirable in order to superimpose their common areas and focus the gaze on the regions of interest. Normally, this task is manually carried out by the expert clinician, but it turns out to be tedious and time-consuming. Here, we present a three-stage methodology for robust multimodal registration of FA and superficial plexus OCTA images. The first one is a preprocessing stage devoted to reducing the noise and segmenting the main vessels in both types of images. The second stage uses the vessel information to do an approximate registration based on template matching. Lastly, the third stage uses an evolutionary algorithm based on differential evolution to refine the previous registration and obtain the optimal registration. The method was evaluated in a dataset with 172 pairs of FA and OCTA images, obtaining a success rate of 98.8%. The best mean execution time of the method was less than 5 s per image.

Correlation of Retinal Thickness and Swept-Source Optical Coherence Tomography Angiography Derived Vascular Changes in Patients with Neovascular Age-Related Macular Degeneration

Purpose: The aim of this study was to investigate whether structural OCT changes, in specific retinal thickness, is associated with the vascular response within the nAMD CNV lesion. In other words, whether SSOCTA derived parameters may prove suitable to assess CNV activity in future.Methods: During the first 3 months patients were prospectively followed with visits at days 7, and 14 after each anti-VEGF treatment up to day 90. At baseline, day 30 and 60 Aflibercept was administered. OCT-derrived retinal thickness (RT) and OCTA-derived CNV lesion parameters (vessel area [VA]), total vessel length [TVL], total number of junctions [TNJ], junction density [JD]) were determined. Parameters were exported from SSOCT/A (PlexElite, Zeiss) images using the semi-automated AngioTool software. Additionally, the superficial and deep vascular plexus fractal dimension of the para- and perifoveal region were identified. Consequently, all OCTA derived parameters were correlated with RT.Results: 16 consecutive patients presenting with treatment-naïve, SSOCTA-positive CNV lesions were included. A weak to moderate statistically significant correlation was found between the mean RT of the inner as well as the outer ETDRS ring with the SSOCTA-derived vascular markers vessel area (VA; r² = -0.38, p < .001; r² = -0.47, p < .001, respectively), total vessel length, (TVL; r² = -0.38, p < .001; r² = -0.48, p < .001, respectively) and total number of junctions (TNJ; r² = -0.35, p < .001; r² = -0.44, p < .001, respectively). Junctions density (JD), and all variables based on fractal dimension (FD) did not show statistically significant correlations with retinal thickness measurements.Conclusions: In summary, we could confirm a moderate, however, statistically significant correlation between mean para- and perifoveal retinal thickness and the SSOCTA derived vascular parameters VA, TVL, and TNJ. This leads us to the conclusion that an SSOCTA-based activity analysis of the CNV complex is not yet a substitute for retinal thickness or in-depth fluid analysis in patients with nAMD.

Profiling neovascular age-related macular degeneration choroidal neovascularization lesion response to anti-vascular endothelial growth factor therapy using SSOCTA

Purpose

To identify the changes in distinct vascular parameters of choroidal neovascularization (CNV) in eyes with treatment‐naïve neovascular age‐related macular degeneration (nAMD) during the primary response to anti‐VEGF therapy using aflibercept.

Methods

Patients were prospectively followed during the first 3 months according to a standardized protocol with mandatory visits at days 7 and 14 after each anti‐VEGF treatment up to day 90. Fourteen eyes were seen in addition at days 1 and 3 post‐initial injection. Aflibercept was administered at baseline (BL), day 30 and 60. 6 × 6mm SSOCTA (PlexElite, Zeiss) images were acquired. Using the semi‐automated AngioTool, CNV area, vessel area, vessel density (VD), the number of junctions, junctions density, total vessel length, average vessel length, total number of endpoints and lacunarity were assessed.

Results

Thirty‐two consecutive patients presenting with treatment‐naïve, SSOCTA‐positive CNV lesions were included. Close follow‐up showed a characteristic neovascular response curve with a dynamic decrease in lesion size within days and a reactive increase following 2 weeks after initial treatment. An undulating pattern was seen for all neovascular parameters except for vascular density, with variable statistical significance. Due to a flattening of the therapeutic response as early as after the second treatment, CNV lesion size and most of the related parameters had an increase in activity above baseline values at the end of the loading phase. Lesion size was the leading feature of reactivation by a mean increase of 19.3% after three monthly aflibercept injections. Subgroup analysis based on lesion size revealed a significant correlation between best‐corrected visual acuity and quantitative change in lesion size over time, but not baseline size.

Conclusions

Using SSOCTA, a morphologic neovascular response pattern can be identified in anti‐VEGF treatment of CNV. A synchronized early decrease and consecutive reactivation in a large spectrum of neovascular biomarkers including size and internal structure are visualized in a qualitative and quantitative manner. SSOCTA analyses allow new insights in CNV morphology changes and therapeutic response.

Application of Deep Learning for Diagnosing, Classifying, and Treating Age-Related Macular Degeneration

Age-related macular degeneration (AMD) affects nearly 200 million people and is the third leading cause of irreversible vision loss worldwide. Deep learning, a branch of artificial intelligence that can learn image recognition based on pre-existing datasets, creates an opportunity for more accurate and efficient diagnosis, classification, and treatment of AMD on both individual and population levels. Current algorithms based on fundus photography and optical coherence tomography imaging have already achieved diagnostic accuracy levels comparable to human graders. This accuracy can be further increased when deep learning algorithms are simultaneously applied to multiple diagnostic imaging modalities. Combined with advances in telemedicine and imaging technology, deep learning can enable large populations of patients to be screened than would otherwise be possible and allow ophthalmologists to focus on seeing those patients who are in need of treatment, thus reducing the number of patients with significant visual impairment from AMD.