Retinal Optical Coherence Tomography Research Articles

CLINICALLY RELEVANT POSTERIOR VITREOUS DETACHMENT STAGING USING CIRCUMPAPILLARY AND MACULAR VOLUME OPTICAL COHERENCE TOMOGRAPHY.

This study was designed to investigate retinal nerve fiber layer circumpapillary optical coherence tomography to determine posterior vitreous detachment (PVD) status and to develop a clinically relevant PVD grading scale based on retinal nerve fiber layer circumpapillary optical coherence tomography to determine the incidence of PVD by age and association with vitreomacular traction disorders. Ophthalmic images and medical records of patients with retinal diseases were retrospectively analyzed by three masked graders using retinal nerve fiber layer circumpapillary optical coherence tomography and macular optical coherence tomography. Based on PVD status, eyes were categorized into five newly defined PVD stages. Among 2002 eyes, PVD stages were as follows: A) 25 (1.25%); B) 725 (36.21%); C-) 248 (12.39%); C+) 151 (7.54%); D) 851 (42.51%); X) 2 (0.1%). Posterior vitreous detachment was correlated with advanced age (P < 0.0001). Limited separation or partial separation between lamella within the posterior vitreous cortex (Stage B) was noted early (68% of eyes <18 years). Overall, 34% of eyes >70 years did not exhibit complete PVD. Of 75 eyes with tractional vitreoretinal disorders, 64 (85.3%) were Stage C-/C+, identifying Stage C as the high-risk "complication" stage. Imaging analyses using retinal nerve fiber layer circumpapillary optical coherence tomography and macular optical coherence tomography scans in conjunction allow rapid assessment of the PVD stage. These techniques can assist clinicians and surgeons in counseling patients and planning surgical approaches. Observations confirmed the progression of PVD through predictable stages and the progression of PVD with age.

Retinal optical coherence tomography measures in multiple sclerosis: a systematic review and meta-analysis.

Spectral domain-optical coherence tomography plays a crucial role in the early detection and monitoring of multiple sclerosis (MS) pathophysiology. We aimed to quantify differences in retinal layer measures among different groups of MS and explored different variables that correlate with retinal measures. This study was reported according PRISMA guidelines. A comprehensive search was done across PubMed, Embase, and Google Scholar. The mean difference in thickness of retinal layers and macular volume was assessed. Meta-regression was done to assess the sources of heterogeneity. A total of 100 articles were included in the meta-analyses. The peripapillary retinal nerve fiber layer (pRNFL) thickness significantly decreased in the MSON (MD: -16.44, P < 0.001), MSNON (MD: -6.97, P < 0.001), and PMS (MD: -11.35, P < 0.001) versus HC. The macular RNFL was lower among the MSON (MD: -6.24, P = 0.013) and MSNON (MD: -3.84, P <0.001) versus HC. Macular ganglion cell layer and inner plexiform layer (GCIPL) was thinner among MSON (MD: -14.83, P <0.001), MSNON (MD: -6.38, P < 0.001), and PMS (MD: -11.52, P < 0.001) compared with control eyes. Inner nuclear layer (INL) was higher in the MSON (MD: 0.49, P < 0.001) versus HC. Outer nuclear layer (ONL) thickness significantly lower in the MSNON (MD: -1.15, P = 0.019) versus HC. Meta-regression showed that disease duration, age, EDSS score, and percentage of patients taking DMT are all negatively correlated with pRNFL and GCIPL thickness; however, female gender was correlated with less atrophy. As conclusion, the study highlights substantial thinning in the pRNFL and macular GCIPL between MS versus controls. INL as valuable parameter for capturing inflammatory disease activity.

Polarization-resolved analysis of outer retinal bands: investigating ballistic and multiply scattered photons using full-field swept-source optical coherence tomography

Precise interpretation of the anatomical origins of outer retinal optical coherence tomography (OCT) presents technical challenges owing to the delicate nature of the retina. To address this challenge, our study introduces a novel polarization-sensitive full-field swept-source OCT (FF-SS-OCT) that provides parallel-polarization and cross-polarization OCT measurements, predominantly capturing ballistically reflected photons and multiply scattered photons, respectively. Notably, parallel-polarization OCT unveils layer-like structures more effectively, including the inner plexiform layer (IPL) sub-layers, outer plexiform layer (OPL) sub-layers (in rod-dominant regions), and rod/cone outer segment (OS) tips, compared to cross-polarization OCT, where such sub-layers are not visible. Through a comparative analysis of parallel-polarization and cross-polarization OCT images of the outer retina, we discovered that the 2nd outer retinal OCT band results from contributions from both the ellipsoid zone (EZ) and the inner segment/outer segment (IS/OS) junction. Similarly, the 3rd outer retinal OCT band appears to reflect contributions from both the interdigitation zone (IZ) and photoreceptor OS tips. This polarization-sensitive approach advances our understanding of the origins of outer retinal OCT signals and proposes potential new biomarkers for assessing retinal health and diseases.

Eye stroke protocol in in the emergency department

BackgroundVisual outcomes of acute central and branch retinal artery occlusions (CRAO/BRAO) are poor and acute treatment options are limited by delayed diagnosis. In the hyper-acute setting, the ocular fundus may appear “normal”, making recognition challenging, but is facilitated by retinal optical coherence tomography (OCT), which is seldom available in emergency departments (ED). We evaluated the use of non-mydriatic ocular fundus photographs (NMFP) combined with OCT to facilitate ultra-rapid remote diagnosis and stroke alert for patients with acute vision loss presenting to the ED. MethodsProspective evaluation of all CRAO/BRAO between 06/06/2023-06/06/2024 who had NMFP-OCT in our general ED affiliated with a stroke center. ResultsOver 1 year, 22 patients were diagnosed with CRAO, 4 with BRAO. Five patients presented within 4.5 hours of vision loss onset, 6 within 4.5 to ≤12 hours and 15 within >12 to 24 hours. On average, NMFP-OCT was performed within 141 minutes of presentation to the ED (range 27- 422 minutes). Diagnosis of acute RAO was made remotely with NMFP-OCT within 4.5 hours in 4 patients, 2 of whom received intravenous thrombolysis. Of the 9 patients with NMFP-OCT within 12 hours of symptom onset, 5 patients had subtle retinal whitening on color fundus photograph, but all had OCT inner retinal hyper-reflectivity/edema. ConclusionImplementation of NMFP-OCT in a general ED enables rapid remote diagnosis of CRAO/BRAO and facilitates initiation of an eye stroke protocol in acute patients. OCT complements color fundus photography and provides greater diagnostic accuracy in hyperacute cases with near-normal appearing ocular fundi.

OUTER RETINAL MICROCAVITATIONS IN RETINITIS PIGMENTOSA : A Novel Optical Coherence Tomography Finding Common in RP1 -Related Retinopathy.

To describe a novel optical coherence tomography (OCT) finding of outer retina microcavitations in RP1 -related retinopathy and other retinal degenerations. Medical charts and OCT images of 28 patients with either autosomal dominant retinitis pigmentosa or autosomal recessive retinitis pigmentosa RP1 -related retinopathy were reviewed. Outer retina microcavitations were defined as hyporeflective OCT structures of at least 30 µ m in diameter between the ellipsoid zone and retinal pigment epithelium. Comparison was made based on the following metrics: (1) functional measures including best-corrected visual acuity and color discrimination errors on D-15 test; and (2) structural measures, including central subfield, average macular thickness, and preserved transfoveal ellipsoid zone width. Mann-Whitney tests were used for comparisons with significance set at P < 0.05. The specificity of microcavitations for RP1 -related retinopathy was estimated against 26 patients with non- RP1 retinitis pigmentosa. Among 15 included patients, microcavitations were found in at least one eye of all patients with arRP and 7/12 (58%) of patients with adRP. Patients with adRP and microcavitations were older at the time of examination (51 vs. 43 years of age; P = 0.04) and their eyes demonstrated worse best-corrected visual acuity (0.09 vs. 0 logMAR; P = 0.008), reduced central subfield (256 vs. 293 µ m; P = 0.01), average macular thickness (241 vs. 270 µ m; P = 0.02), and shorter transfoveal ellipsoid zone widths (1.67 vs. 4.98 mm; P < 0.0001). The finding of microcavitations showed a specificity of 0.92 for RP1 -related retinopathy. A novel OCT finding of outer retina microcavitations was commonly observed in patients with RP1 -related retinopathy. Eyes with outer retinal OCT microcavitations had worse visual function and more affected central retinal structure.

Advanced OCTA imaging segmentation: Unsupervised, non-linear retinal vessel detection using modified self-organizing maps and joint MGRF modeling

Background and Objective:This paper proposes a fully automated and unsupervised stochastic segmentation approach using two-level joint Markov-Gibbs Random Field (MGRF) to detect the vascular system from retinal Optical Coherence Tomography Angiography (OCTA) images, which is a critical step in developing Computer-Aided Diagnosis (CAD) systems for detecting retinal diseases. Methods:Using a new probabilistic model based on a Linear Combination of Discrete Gaussian (LCDG), the first level models the appearance of OCTA images and their spatially smoothed images. The parameters of the LCDG model are estimated using a modified Expectation Maximization (EM) algorithm. The second level models the maps of OCTA images, including the vascular system and other retina tissues, using MGRF with analytically estimated parameters from the input images. The proposed segmentation approach employs modified self-organizing maps as a MAP-based optimizer maximizing the joint likelihood and handles the Joint MGRF model in a new, unsupervised way. This approach deviates from traditional stochastic optimization approaches and leverages non-linear optimization to achieve more accurate segmentation results. Results:The proposed segmentation framework is evaluated quantitatively on a dataset of 204 subjects. Achieving 0.92 ± 0.03 Dice similarity coefficient, 0.69 ± 0.25 95-percentile bidirectional Hausdorff distance, and 0.93 ± 0.03 accuracy, confirms the superior performance of the proposed approach. Conclusions:The conclusions drawn from the study highlight the superior performance of the proposed unsupervised and fully automated segmentation approach in detecting the vascular system from OCTA images. This approach not only deviates from traditional methods but also achieves more accurate segmentation results, demonstrating its potential in aiding the development of CAD systems for detecting retinal diseases.

Repeatability of retinal and choroidal optical coherence tomography angiography indices in healthy children and young adults.

To evaluate the repeatability of retinal and choroidal optical coherence tomography angiography (OCT-A) indices among healthy children and compare it to healthy young adults. This prospective study captured 3 mm × 3 mm and 6 mm × 6 mm macular OCT-A scans including superficial and deep retinal layers, choriocapillaris and deep choroid over two visits, 1 week apart at approximately the same time of day, for 22 healthy adults (18-30 years) and 21 children (6-15 years). Magnification and projection-artefact corrected indices extracted using a custom image analysis program and individual biometry were compared between visits using Bland-Altman analysis and intraclass correlation (ICC). Retinal indices included foveal avascular zone metrics, perfusion and vessel density and choroidal indices included choriocapillaris flow deficit metrics and deep choroid perfusion density, in the foveal, parafoveal and perifoveal regions. Repeatability between adults and children was compared with F-test. Bland-Altman analysis showed that the mean differences between repeated OCT-A indices were not significantly different from zero for either of the zones, layers and scan sizes in the two age groups (p > 0.05) except for foveal vessel density and foveal avascular zone perimeter (p = 0.04 for both) of 6-mm-deep retinal layer scans. The ICC ranged between 0.67 and 0.99. Significantly higher variability between visits (p < 0.05) in the indices was noted among adults than children, especially for choroidal indices of larger scan size. The retinal and choroidal OCT-A indices in the foveal, parafoveal and perifoveal zones were repeatable in healthy children except for the foveal vessel density and foveal avascular zone perimeter of the 6-mm-deep retinal layer, which exhibited statistically borderline differences between visits. The adult group showed more variability between visits compared to children, especially in the larger scan size for choroidal OCT-A indices.

Characterizing Retinal Sensitivity and Structure in Congenital Stationary Night Blindness: A Combined Microperimetry and OCT Study.

To investigate the characteristics of microperimetry and optical coherence tomography (OCT) in congenital stationary night blindness (CSNB), as well as their structure-function association. This cross-sectional study included 32 eyes from 32 participants with CSNB, comprising 18 with complete CSNB and 14 with incomplete CSNB, along with 36 eyes from 36 CSNB-unaffected controls matched for age, sex, and spherical equivalent. Using MP-3 microperimetry, central retinal sensitivity was assessed within a 20° field, distributed across six concentric rings (0°, 2°, 4°, 6°, 8°, and 10°). OCT was used to analyze retinal and choroidal thickness. The study aimed to assess the overall and ring-wise retinal sensitivity, as well as choroidal and retinal thickness in CSNB and CSNB-unaffected controls, with a secondary focus on the relationship between retinal sensitivity and microstructural features on OCT. In comparison with CSNB-unaffected subjects, the overall and ring-wise retinal sensitivity as well as choroidal thickness were reduced in patients with CSNB (P < 0.001). Moreover, the central sensitivity in incomplete CSNB group was lower than in complete CSNB group (25.72 ± 3.93 dB vs. 21.92 ± 4.10dB; P < 0.001). The retinal thickness in the CSNB group was thinner outside the fovea compared with the CSNB-unaffected group. Multiple mixed regression analyses revealed that point-to-point retinal sensitivity was significantly correlated with BCVA (P = 0.002) and the corresponding retinal thickness (P = 0.004). Examination of retinal sensitivity and OCT revealed different spatial distribution profiles in CSNB and its subtypes. In CSNB eyes, retinal sensitivity on microperimetry was associated with retinal thickness on OCT.

Analisis Retinal Optical Coherence Tomography (OCT) Untuk Deteksi Kerusakan Retina Menggunakan Metode Machine Learning

This study attempts to use support vector machine and otsu thresholding as proposed algorithm models to classify Retinal optical coherence tomography (OCT) images. In this study, there are two types implemented in classifying retinal image datasets. The first scenario is to classify using the support vector machine algorithm without the otsu thresholding method and the second scenario is to classify using the support vector machine algorithm with the otsu thresholding method with various parameter values. Based on the experimental results, classification of retina image datasets using the support vector machine algorithm without the otsu thresholding method obtained an accuracy of 63.00% while classification using the support vector machine algorithm with the otsu thresholding method with parameter values (0, 255), (50, 255), (100, 255), (150, 255) obtained an accuracy of 59.30%.

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.

Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

Experimental study on treatment of retinitis pigmentosa by inducing Müller cell reprogramming with Lycii Fructus and Salviae Miltiorrhizae Radix et Rhizoma

This study aims to explore the effect of Lycii Fructus and Salviae Miltiorrhizae Radix et Rhizoma(LFSMR), a drug pair possesses the function of nourishing Yin, promoting blood circulation, and brightening the eyes, in treating retinitis pigmentosa(RP)by inhibiting the gliosis of Müller cells(MCs) and inducing their reprogramming and differentiation into various types of retinal nerve cells. Twelve C57 mice were used as the normal control group, and 48 transgenic RP(rd10) mice were randomly divided into the model group, positive control group, and low and high dose LFSMR groups, with 12 mice in each group. HE staining was used to detect pathological changes in the retina, and an electroretinogram was used to detect retinal function. Retinal optical coherence tomography was used to detect retinal thickness and perform fundus photography, and laser speckle perfusion imaging was used to detect local retinal blood flow. Digital PCR was used to detect gene expression related to retinal nerve cells, and immunofluorescence was used to detect protein expression related to retinal nerve cells. LFSMR could significantly improve the pathological changes, increase the amplitude of a and b waves, increase the retinal thickness, restore retinal damage, and increase retinal blood flow in mice with RP lesions. LFSMR could also significantly inhibit the m RNA expression of the glial fibrillary acidic protein( GFAP) during the pathogenesis of RP and upregulate m RNA expression of sex determining region Y box protein 2(SOX2), paired box protein 6(Pax6),rhodopsin, protein kinase C-α(PKCα), syntaxin, and thymic cell antigen 1. 1(Thy1. 1). LFSMR could significantly inhibit GFAP protein expression and enhance protein expression of SOX2, Pax6, rhodopsin, PKCα, syntaxin, and Thy1. 1. It could also reverse the pathological changes in the retina of rd10 mice, improve retinal function and fundus performance, increase retinal thickness, enhance local retinal blood flow, and exert therapeutic effects on RP. The mechanism of action of LFSMR may be related to inhibiting the gliosis of MCs and promoting their reprogramming and differentiation into various types of retinal nerve cells.

Deep Learning–Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4)

PurposeWe introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). DesignRetrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images. ParticipantsA total of 3,456 adults aged between 51 and 102 years of whom OCT images were collected under the PINNACLE project. MethodsOur system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome MeasuresWe checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late stage wet and dry AMD against an established clinical grading system and a demographic baseline model. ResultsOverall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. ConclusionsUsing self-supervised deep learning we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.

Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study

Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

Anomaly Detection and Biomarkers Localization in Retinal Images.

Background: To design a novel anomaly detection and localization approach using artificial intelligence methods using optical coherence tomography (OCT) scans for retinal diseases. Methods: High-resolution OCT scans from the publicly available Kaggle dataset and a local dataset were used by four state-of-the-art self-supervised frameworks. The backbone model of all the frameworks was a pre-trained convolutional neural network (CNN), which enabled the extraction of meaningful features from OCT images. Anomalous images included choroidal neovascularization (CNV), diabetic macular edema (DME), and the presence of drusen. Anomaly detectors were evaluated by commonly accepted performance metrics, including area under the receiver operating characteristic curve, F1 score, and accuracy. Results: A total of 25,315 high-resolution retinal OCT slabs were used for training. Test and validation sets consisted of 968 and 4000 slabs, respectively. The best performing across all anomaly detectors had an area under the receiver operating characteristic of 0.99. All frameworks were shown to achieve high performance and generalize well for the different retinal diseases. Heat maps were generated to visualize the quality of the frameworks' ability to localize anomalous areas of the image. Conclusions: This study shows that with the use of pre-trained feature extractors, the frameworks tested can generalize to the domain of retinal OCT scans and achieve high image-level ROC-AUC scores. The localization results of these frameworks are promising and successfully capture areas that indicate the presence of retinal pathology. Moreover, such frameworks have the potential to uncover new biomarkers that are difficult for the human eye to detect. Frameworks for anomaly detection and localization can potentially be integrated into clinical decision support and automatic screening systems that will aid ophthalmologists in patient diagnosis, follow-up, and treatment design. This work establishes a solid basis for further development of automated anomaly detection frameworks for clinical use.

A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: A preliminary study.

Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans. The cohort consisted of 332 patients, of which 235 were diagnosed with AMD and 97 were controls with no signs of AMD. The genome-wide association studies summary statistics utilized to establish the polygenic risk score (PRS) in relation to AMD were derived from the GERA European study. A PRS estimation based on OCT volumes for both eyes was performed using a proprietary convolutional neural network (CNN) model supported by machine learning models. The method's performance was assessed using numerical evaluation metrics, and the Grad-CAM technique was used to evaluate the results by visualizing the features learned by the model. The best results were obtained with the CNN and the Extra Tree regressor (MAE = 0.55, MSE = 0.49, RMSE = 0.70, R2 = 0.34). Extending the feature vector with additional information on AMD diagnosis, age and smoking history improved the results slightly, with mainly AMD diagnosis used by the model (MAE = 0.54, MSE = 0.44, RMSE = 0.66, R2 = 0.42). Grad-CAM heatmap evaluation showed that the model decisions rely on retinal morphology factors relevant to AMD diagnosis. The developed method allows an efficient PRS estimation from OCT images. A new technique for analysing the association of OCT images with PRS of AMD, using a deep learning approach, may provide an opportunity to discover new associations between genotype-based AMD risk and retinal morphology.

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.

Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography). The Hybrid Model GIGT, which combines Generative Adversarial Networks (GANs), Inception, and Game Theory, is a novel method for diagnosing retinal diseases using OCT pictures. This technique, which is carried out in Python, includes preprocessing images, feature extraction, GAN classification, and a game-theoretic examination. Resizing, grayscale conversion, noise reduction using Gaussian filters, contrast enhancement using Contrast Limiting Adaptive Histogram Equalization (CLAHE), and edge recognition via the Canny technique are all part of the picture preparation step. These procedures set up the OCT pictures for efficient analysis. The Inception model is used for feature extraction, which enables the extraction of discriminative characteristics from the previously processed pictures. GANs are used for classification, which improves accuracy and resilience by adding a strategic and dynamic aspect to the diagnostic process. Additionally, a game-theoretic analysis is utilized to evaluate the security and dependability of the model in the face of hostile attacks. Strategic analysis and deep learning work together to provide a potent diagnostic tool. This suggested model's remarkable 98.2% accuracy rate shows how this method has the potential to improve the detection of retinal diseases, improve patient outcomes, and address the worldwide issue of visual impairment.

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Ensemble learning for retinal disease recognition under limited resources.

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of retinal OCT images often presents challenges stemming from privacy concerns and the resource-intensive labeling procedures, which contradicts the prevailing notion that DL models necessitate substantial data volumes for achieving superior performance. Moreover, limitations in available computational resources constrain the progress of high-performance medical artificial intelligence, particularly in less developed regions and countries. This paper introduces a novel ensemble learning mechanism designed for recognizing retinal diseases under limited resources (e.g., data, computation). The mechanism leverages insights from multiple pre-trained models, facilitating the transfer and adaptation of their knowledge to retinal OCT images. This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch. Comprehensive experimentation on real-world datasets demonstrates that the ensemble models constructed by the proposed ensemble method show superior performance over the baseline models under sparse labeled data, especially the triple ensemble model, which achieves the accuracy of 92.06%, which is 8.27%, 7.99%, and 11.14% better than the three baseline models, respectively. In addition, compared with the three baseline models learned from scratch, the triple ensemble model has fewer trainable parameters, only 3.677M, which is lower than the three baseline models of 8.013M, 4.302M, and 20.158M, respectively.

Disease Course of Clinically Isolated Optic Neuritis.

Optic neuritis is the most common optic neuropathy in young adults and a frequent manifestation of multiple sclerosis. Its clinical course is pertinent to the design of visual pathway neuroprotection trials. This is a secondary analysis of longitudinal data from the TONE trial, which included 103 patients from 12 German academic tertiary centers with acute unilateral optic neuritis as a clinically isolated syndrome and baseline high-contrast visual acuity <0.5 decimal. Patients were randomized to 1,000 mg methylprednisolone i.v./d plus either erythropoietin (33,000 IU/d) or placebo (saline solution) for 3 days. They were followed up at standardized intervals with a battery of tests including high-contrast visual acuity, low-contrast letter acuity, contrast sensitivity, visual fields, visual evoked potentials, and retinal optical coherence tomography. At 6 months, participants answered a standardized questionnaire on vision-related quality of life (NEI-VFQ 25). We describe the disease course with mixed-effects piecewise linear models and calculate structure-function correlations using Pearson r. Because erythropoietin had no effect on the visual system, we use pooled (treatment-agnostic) data. Patients experienced initial rapid and then decelerating improvements of visual function with thinning of inner and thickening of outer retinal layers. At 6 months, visual parameters were positively correlated with inner and negatively correlated with outer retinal thickness changes. Peripapillary retinal nerve fiber layer thinning predominantly occurred in sectors without previous swelling. At 6 months, macular ganglion cell and inner plexiform layer thinning was weakly correlated with the P100 peak time (r = -0.11) and moderately correlated with the amplitude of visual evoked potentials (r = 0.35). Only functional outcomes were at least moderately correlated with vision-related quality of life. The longitudinal data from this large study cohort may serve as a reference for the clinical course of acute optic neuritis. The pattern of correlation between visual evoked potentials and inner retinal thinning may argue that the latter is mostly due to ganglion cell loss, rather than dysfunction. Visual pathway neuroprotection trials with functional outcomes are needed to confirm that candidate drugs will benefit patients' vision-related quality of life. ClinicalTrials.gov, NCT01962571.

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) , BCNN (VGG19) , and BCNN (Inception_V3) , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.