Fundus Features Research Articles

Automated measurement and correlation analysis of fundus tessellation and optic disc characteristics in myopia

This study aims to quantify fundus tessellated (FT) density and optic disc (OD) morphology using deep learning (DL) techniques and to investigate the correlations between these fundus characteristics and refractive function in young patients with myopia. We constructed two DL-based segmentation models to delineate the FT, OD, peripapillary atrophy (PPA), and macula at a pixel-level resolution. The study sought to identify differences in fundus characteristics between eyes categorized as having high myopia versus mild or moderate myopia. Furthermore, the correlation between fundus measurements and various ocular parameters was statistically analyzed. Correlation analysis indicated that the spherical equivalent and axial length were significantly associated with all fundus measurements (p < 0.001). Additionally, corneal curvature (K1, K2), lens thickness, and foveal thickness exhibited significant correlations with some of the fundus measurements at a 0.01 significance level. Using DL algorithms, it is feasible to automatically quantify FT and OD characteristics in young myopic patients. The study findings suggest that both FT and OD characteristics are highly correlated with the severity of myopia, particularly as it progresses from mild or moderate to high levels. Moreover, a significant relationship exists between most of these fundus characteristics and a spectrum of refractive function parameters.

Investigating Microperimetric Features in Bietti Crystalline Dystrophy Patients: A Cross-Sectional Longitudinal Study in a Large Cohort.

To assess the clinical and genetic characteristics of patients with Bietti crystalline dystrophy (BCD) with a focus on potential of microperimetry in monitoring macular function. A total of 208 genetically-confirmed BCD patients were enrolled in this retrospective study. The patients were categorized into subgroups based on their fundus characteristics (fovea sparing and fovea involved), optical coherence tomography (OCT) findings (presence/absence of retinal pigment epithelium [RPE] or ellipsoid zone [EZ] line at the fovea/parafovea), and genetic profiles (Mis/Mis, Tru/Mis, Tru/Tru). Fixation patterns were analyzed, and macular sensitivity (MS) parameters were compared among different groups. Longitudinal analysis was performed to calculate the annual changes in MS parameters. Correlation between genotype and phenotype were further investigated by analyzing cumulative incidence of vision impairment among different genotypic groups. Patients with well-preserved RPE or EZ at the foveal/parafoveal region exhibited higher MS. Notably, there was a decline in sensitivity parameters, with a decrease of -2.193dB/year (95% confidence interval [CI] -4.292 to -0.095, P = 0.041) at the fovea and -1.353dB/year (95% CI -2.047 to -0.659, P < 0.001) in average sensitivity. An age-adjusted comparison of sensitivity among genotypic groups and cumulative incidence analyses showed no association between genotypic groups and vision loss. Microperimetry proves to be one of a credible tool for detecting macular functional changes in BCD patients. BCD patients with different genotypes may have similar disease progression.

Characteristics of the fundus and optical coherence tomography angiography metrics in myopic patients with myelinated retinal nerve fibers

AimsTo investigate the fundus characteristics and macular optical coherence tomography angiography (OCTA) metrics in patients with myelinated retinal nerve fibers (MRNF) and to identify the associations among myopic parameters, fundus metrics, and OCTA metrics.MethodsThe single-center case-control study included 15 patients with monocular MRNF and 15 patients without MRNF, matched for age, affected eyes’ spherical equivalent error (SER), and axial length (AL) between May 2021 and December 2021. Fundus features, including the entire MRNF area (Area1), the denser MRNF area (Area2), and the shortest distance from the fovea to the MRNF border (Df−m), along with ocular parameters, including SER, AL, best corrected visual acuity (BCVA), and OCTA metrics at the fovea, including choroidal (ChT) and retinal (ReT) thicknesses at the fovea, the foveal avascular zone area (FAZ), foveal avascular zone perimeter (FAZP), FAZ-circularity index (FAZ-CI), and foveal vessel density in a 300-µm wide region (FD-300), were assessed.ResultsThe SER, AL, and BCVA were significantly different, and the ChT was thinner in the affected eyes than in the contralateral eyes. Compared with matched eyes, affected eyes had reduced BCVA and thinner ReT. In the affected eyes, both Area1 and Area2 were negatively correlated with SER (r1 = -0.84, r2 = -0.65), △SER (r1 = -0.86, r2 = -0.61), ReT at the vertical superior 3 mm (VS) (r1 = -0.78, r2 = -0.67), and ChT_VS (r1 = -0.64, r2 = -0.62), and positively correlated with AL (r1 = 0.82, r2 = 0.52) and BCVA (r1 = 0.54, r2 = 0.59). Df−m was negatively correlated with BCVA (r = -0.57).ConclusionThe presence of MRNF affects BCVA and ReT at the fovea. The area of the MRNF, as well as the distance from the MRNF to the fovea, are closely associated with the affected eyes’ BCVA, SER, AL, and the degree of anisometropia.

Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9.

Computer vision is a powerful tool in medical image analysis, supporting the early detection and classification of eye diseases. Diabetic retinopathy (DR), a severe eye disease secondary to diabetes, accompanies several early signs of eye-threatening conditions, such as microaneurysms (MAs), hemorrhages (HEMOs), and exudates (EXs), which have been widely studied and targeted as objects to be detected by computer vision models. In this work, we tested the performances of the state-of-the-art YOLOv8 and YOLOv9 architectures on DR fundus features segmentation without coding experience or a programming background. We took one hundred DR images from the public MESSIDOR database, manually labelled and prepared them for pixel segmentation, and tested the detection abilities of different model variants. We increased the diversity of the training sample by data augmentation, including tiling, flipping, and rotating the fundus images. The proposed approaches reached an acceptable mean average precision (mAP) in detecting DR lesions such as MA, HEMO, and EX, as well as a hallmark of the posterior pole of the eye, such as the optic disc. We compared our results with related works in the literature involving different neural networks. Our results are promising, but far from being ready for implementation into clinical practice. Accurate lesion detection is mandatory to ensure early and correct diagnoses. Future works will investigate lesion detection further, especially MA segmentation, with improved extraction techniques, image pre-processing, and standardized datasets.

Assessment of Baseline Ultrawidefield Fluorescein Angiographic Quantitative Leakage Parameters with Ultrawidefield Fundus Features and Clinical Parameters in Diabetic Retinopathy in Protocol AA

PurposeEvaluate quantitative leakage parameters on ultra-widefield fluorescein angiography (UWF-FA) images and explore their association with Diabetic Retinopathy Severity Scale (DRSS), predominantly peripheral lesions (PPLs), visual acuity, and clinical characteristics. DesignA post-hoc analysis of baseline UWF-FA images in the DRCR Retina Network observational study Protocol AA. ParticipantsN = 575 eyes from 384 adults across 38 sites in the US and Canada with gradable UWF-FA. MethodsA machine learning-enhanced feature extraction platform provided initial leakage segmentation of UWF-FA images sequentially reviewed and corrected by two certified readers for segmentation accuracy. UWF-FA leakage was measured in five retinal zones: panretinal (whole retina), central macular (3-disc diameter fovea-centered circle), posterior pole (6-disc diameter fovea-centered circle), peripheral (outside 6-disc diameter circle) and widefield far peripheral (outside 9-disc diameter circle); associations with clinical factors were evaluated with marginal beta regression models. Main Outcome MeasuresUWF-FA leakage index, calculated as the area with leakage divided by the analyzable retinal area. ResultsThe mean quantitative leakage index was 3.5% for panretinal, 6.6% for macular, 4.8% for posterior pole, 3.3% for peripheral and 2.8% for wide-field far peripheral retinal zones. Panretinal leakage was associated with DRSS (mean 2.2% for no to mild NPDR, 3.4% for moderate NPDR, 4.2% for moderately severe NPDR, 4.8% for severe NPDR and 5.1% for PDR; P<.001), HbA1c (3.2% for HbA1c <8% vs. 3.8% for HbA1c ≥8%; P=.01 for continuous HbA1c), visual acuity (3.3% for 20/25 or better vs. 4.7% for 20/32 or worse; continuous P<.001), and UWF-FA-PPL types of IRMA (4.3% vs. 3.3%; P=.005) or NVE (5.7% vs. 3.4%; P=.003). DRSS was also statistically significant for leakage within all retinal zones (P<.001); eyes with non-central DME versus no DME had higher mean leakage in the central macular (11.2% vs. 5.9%; P=0.005) and posterior pole regions (9.2% vs. 4.2%; P=.002). ConclusionQuantitative UWF-FA leakage analysis identified associations between leakage and DRSS, visual acuity, and presence of DME. In the future, quantitative UWF-FA leakage parameters may be explored as potential biomarkers for disease progression risk.

Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism

The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age.

Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis: A Cross-Sectional Multicenter Study

In ophthalmology, the quality of fundus images is critical for accurate diagnosis, both in clinical practice and in artificial intelligence (AI)-assisted diagnostics. Despite the broad view provided by ultrawide-field (UWF) imaging, pseudocolor images may conceal critical lesions necessary for precise diagnosis. To address this, we introduce UWF-Net, a sophisticated image enhancement algorithm that takes disease characteristics into consideration. Using the Fudan University ultra-wide-field image (FDUWI) dataset, which includes 11 294 Optos pseudocolor and 2415 Zeiss true-color UWF images, each of which is rigorously annotated, UWF-Net combines global style modeling with feature-level lesion enhancement. Pathological consistency loss is also applied to maintain fundus feature integrity, significantly improving image quality. Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization (CLAHE) and structure and illumination constrained generative adversarial network (StillGAN), delivering superior retinal image quality, higher quality scores, and preserved feature details after enhancement. In disease classification tasks, images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN, demonstrating a 4.62% increase in sensitivity (SEN) and a 3.97% increase in accuracy (ACC). In a multicenter clinical setting, UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors, and yielded a significant reduction in diagnostic time ((13.17 ± 8.40) s for UWF-Net enhanced images vs (19.54 ± 12.40) s for original images) and an increase in diagnostic accuracy (87.71% for UWF-Net enhanced images vs 80.40% for original images). Our research verifies that UWF-Net markedly improves the quality of UWF imaging, facilitating better clinical outcomes and more reliable AI-assisted disease classification. The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

Genotype-phenotype associations in CRB1 bi-allelic patients: a novel mutation, a systematic review and meta-analysis.

The goal of the study was to search for novel bi-allelic CRB1 mutations, and then to analyze the CRB1 literature at the genotypic and phenotypic levels. We screened various variables such as the CRB1 mutation types, domains, exons, and genotypes and their relation with specific ocular phenotypes. An emphasis was given to the bi-allelic missense and nonsense mutations because of their high prevalence compared to other mutation types. Finally, we quantified the effect of various non-modifiable factors over the best-corrected visual acuity oculus uterque (BCVA OU) using multivariate linear regression models and identified genetic interactions. A novel bi-allelic missense in the exon 9 of CRB1; c.2936G > A; p.(Gly979Asp) was found to be associated with rod-cone dystrophy (RCD). CRB1 mutation type, exons, domains, and genotype distribution varied significantly according to fundus characteristics, such as peripheral pigmentation and condition, optic disc, vessels, macular condition, and pigmentation (P < 0.05). Of the 154 articles retrieved from PubMed, 96 studies with 439 bi-allelic CRB1 patients were included. Missense mutations were significantly associated with an absence of macular pigments, pale optic disc, and periphery pigmentation, resulting in a higher risk of RCD (P < 0.05). In contrast, homozygous nonsense mutations were associated with macular pigments, periphery pigments, and a high risk of LCA (P < 0.05) and increased BCVA OU levels. We found that age, mutation types, and inherited retinal diseases were critical determinants of BCVA OU as they significantly increased it by 33% 26%, and 38%, respectively (P < 0.05). Loss of function alleles additively increased the risk of LCA, with nonsense having a more profound effect than indels. Finally, our analysis showed that p.(Cys948Tyr) and p.(Lys801Ter) and p.(Lys801Ter); p.(Cys896Ter) might interact to modify BCVA OU levels. This meta-analysis updated the literature and identified genotype-phenotype associations in bi-allelic CRB1 patients.

A Deep Learning Model for Neural Network Optimization for Glaucoma Classification Using Fundus and Oct Feature Fusion

Irreversible vision loss is a common consequence of glaucoma, demands accurate and timely diagnosis for effective management. This research aims to enhance glaucoma classification accuracy by fusing information from two distinct imaging modalities like using deep learning explores the fusion of these modalities through an innovative neural network architecture with optimization. This approach combines Deep Stochastic Variational Autoencoder Convolution Neural Networks (DSVAECNN) and Adam optimization techniques to enable robust and accurate classification of glaucoma. A multi-path architecture is designed to accommodate both imaging as optical coherence tomography (OCT) radiomics features and fundus morphological features simultaneously. To ensure the effectiveness of the model, this study investigates the implementation of advanced optimization algorithm such as Adam, to expedite convergence and alleviate the risk of over fitting. The resulting model demonstrates improved generalization capabilities, critically for accurate diagnosis across diverse patient populations. A comprehensive OCT and fundus images dataset is used to evaluate the proposed approach from a representative cohort of glaucoma patients and healthy individuals. Quantitative various metrics including sensitivity, accuracy and specificity are employed toward appraise the accomplishment of the fusion-based classification model. Comparisons with current techniques show the dominance of the proposed move toward in accurately detecting glaucoma cases. This research provides the advancement of glaucoma diagnosis by effectively harnessing the synergy between fundus image and OCT scans and findings helpful information for clinics, ultimately facilitating early detection and personalized management of glaucoma, thus preserving vision for affected individuals.

Protocol for the combined cardiometabolic deep phenotyping and registry-based 20-year follow-up study of the Inter99 cohort

IntroductionThe population-based Inter99 cohort has contributed extensively to our understanding of effects of a systematic screening and lifestyle intervention, as well as the multifactorial aetiology of type 2 diabetes (T2D)...

Ocular fundus changes and association with systemic conditions in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs and systems. Ocular involvement is estimated to manifest in one-third of individuals with SLE, of which lupus retinopathy and choroidopathy represent the severe subtype accompanied by vision impairment. Advancements in multimodal ophthalmic imaging have allowed ophthalmologists to reveal subclinical microvascular and structural changes in fundus of patients with SLE without ocular manifestations. Both ocular manifestations and subclinical fundus damage have been shown to correlate with SLE disease activity and, in some patients, even precede other systemic injuries as the first presentation of SLE. Moreover, ocular fundus might serve as a window into the state of systemic vasculitis in patients with SLE. Given the similarities of the anatomy, physiological and pathological processes shared among ocular fundus, and other vital organ damage in SLE, such as kidney and brain, it is assumed that ocular fundus involvement has implications in the diagnosis and evaluation of other systemic impairments. Therefore, evaluating the fundus characteristics of patients with SLE not only contributes to the early diagnosis and intervention of potential vision damage, but also holds considerate significance for the evaluation of SLE vasculitis state and prediction of other systemic injuries.

Double Hyperautofluorescence Rings as a Sign of CFAP410-related Retinopathy.

Variants in CFAP410 have been reported to cause retinal dystrophy with or without systemic symptoms. This study was designed to characterize the fundus changes of patients with biallelic variants in CFAP410. Variants in CFAP410 were identified through whole exome sequencing and targeted exome sequencing of 10,530 probands. Biallelic variants in CFAP410 were evaluated by comprehensive in silico analysis and confirmed by Sanger sequencing and segregation analysis. Ocular phenotypes including fundus photographs, scanning laser ophthalmoscopy, autofluorescence images, ERG, and optical coherence tomography were characterized. Nine patients from eight families were homozygotes or compound heterozygotes for a total of four variants in CFAP410, including c.144-6_159del (novel), c.340_351dup, c.347C>T, and c.545+1G>A. Three patients were diagnosed with cone-rod dystrophy, and the remaining six patients with RP. Among eight patients performed with ultra-wide scanning laser ophthalmoscopy, double hyperautofluorescence rings inside and outside of the macular vascular arcades were observed in six patients, and the remaining two older patients demonstrated single hyperautofluorescence ring surrounded by pigmentation. CFAP410-associated retinopathy in early stage was generally tapetoretinal degeneration without noticeable bone spicule pigmentation, with more severe degeneration in the inferior nasal retina. ERG recordings delineated a severely reduced cone response and mildly to severely reduced rod response. Posterior staphyloma was seen in seven patients who underwent optical coherence tomography examinations. The present study demonstrates the fundus characteristics of patients with biallelic variants in CFAP410 and expands the genotype-phenotype spectrum of CFAP410-related retinal degeneration, in which posterior staphyloma together with double hyperautofluorescence rings might be common peculiar signs.

Incorporating medical domain knowledge into data-driven method: A vessel attention guided multi-granularity network for automatic cataract classification

Early cataract screening is essential to help patients prevent irreversible vision loss. Existing data-driven methods can automatically extract deep fundus features for automatic cataract classification. However, due to the lack of medical domain knowledge, these deep features may contain large pathological irrelevant information, thus reducing the classification accuracy of data-driven methods. To fully take the advantages of domain knowledge to enhance the discriminative representation and avoid the irrelevant impact, we comprehensively investigate how to incorporate it into data-driven models. Considering the vessel information and multi-granularity representation are two key points of domain knowledge for cataract diagnosis, we propose a vessel attention guided multi-granularity network (VAM-Net), which contains three main components. Specifically, the vessel-level segmentation subnet is firstly designed by leveraging U-shape segmentation network. The refined vessel information obtained in this step can be regarded as visual attention to enable deep network extract discriminative features. With the guidance of vessel attention, the global-local classification subnet is further proposed to eliminate the interference of irrelevant information and enhance the multi-granularity feature learning (including global structural features and local subtle features). Finally, the multiple levels predictions are integrated into the final cataract diagnosis decision via constructing multi-granularity ensemble network. In this manner, both domain knowledge and deep networks are organically combined for pursuing better diagnosis performance. By setting extensive comparative and ablation experiments, we evaluate the effectiveness of the proposed VAM-Net on the real-world cataract dataset (92.78% detection accuracy and 87.68% grading accuracy).

Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer

This study attempts to synthesize a realistic-looking fundus image from a morphologically changed vessel structure using the newly proposed sharpening and varying vessels technique (SVV). This technique sharpens the reconstructed vessels and introduces variation to their structure to generate multiple images from a single input mask. This helps to reduce the reliance on expensive and scarce annotated medical data. The study also aims to overcome the limitations of current methods, such as unrealistic optic disc boundaries, extreme vessel tortuosity, and missed optic discs. This is mainly due to the fact that existing models penalize their weights based on the difference between real and synthetic images using only a single mask. Therefore, their emphasis is on generating the input mask while disregarding other important fundoscopic features. Inspired by the recent progress in Generative Adversarial Nets (GANs) and Variational Autoencoder (VAE), the proposed approach was able to preserve the geometrical shape of critical fundus characteristics. Visual and quantitative results indicate that the produced images are considerably distinct from the ones used for training. However, they also exhibit anatomical coherence and a reasonable level of visual. The data utilized in this study and the programming code necessary to recreate the experiment can be accessed at https://github.com/AliSaeed86/SVV_GAN.

Multimodal Analysis on Clinical Characteristics of the Advanced Stage in Myopic Traction Maculopathy.

Here we investigate the clinical features of foveal detachment (FD), full-thickness macular hole (MH), and macular hole retinal detachment (MHRD) in myopic traction maculopathy (MTM). In the retrospective observational case series, 314 eyes of 198 patients with myopic retinoschisis in Beijing Tongren Hospital were enrolled. We recorded gender, age, and axial length and evaluated fundus characteristics using optical coherence tomography. Epiretinal membranes (ERMs), vitreoretinal traction, and paravascular abnormalities (PVAs) were used to describe the vitreoretinal interface condition. Different retinoschisis layers (inner, middle, and outer retinoschisis) and the location with a range of outer retinoschisis were evaluated to reveal the retinal condition. Five patterns of the scleral shape: dome-shaped, sloped toward the optic nerve, symmetrical or asymmetrical around the fovea, and irregular were assessed to show retina-sclera condition. We regarded the FD, full-thickness MH, and MHRD as the advanced stage in MTM. Multivariate logistic regression assessed significant factors for the advanced stage with odds ratios (OR) and 95% confidence intervals (CI). There were 76 eyes with FD, 6 eyes with full-thickness MH, and 7 eyes with MHRD. The mean age was 52.9 ± 12.3years. On univariate analysis, the eyes with the advanced stage were older and had higher rates of ERMs, PVAs, middle retinoschisis, outer retinoschisis, and irregular sclera shape. The number of retinoschisis layers and the grade of outer retinoschisis were higher in eyes with the advanced stage. After multivariate logistic regression, ERMs (OR 1.983; 95% CI 1.093-3.595; P = 0.024), middle retinoschisis (OR 2.967; 95% CI 1.630-5.401; P < 0.001), and higher grades of outer retinoschisis (OR 2.227; 95% CI 1.711-2.898; P < 0.001) remained associated with the advanced stage. ERMs, middle retinoschisis, and more extensive outer retinoschisis were significant characteristics of the advanced stage in MTM.

Characteristics Of Ocular Fundus In Brain Infections At Cipto Mangunkusumo Hospital Jakarta

Background: Brain infections (BI) may cause optic nerve abnormalities. Awareness on optic nerve abnormalities will be useful in assisting further management. The characteristics of the ocular fundus (fundus) in BI have not been widely studied in Indonesia. This study aims to apprehend the depiction of the fundus in BI in Cipto Mangunkusumo Hospital Jakarta (RSCM). Methods: Cross-sectional study conducted on October-December 2021 at RSCM. Images from 20D-lens fundoscopy were captured with a digital camera. Results are analyzed if there are similarities between at least two examiners. Results: There were 49 subjects whose fundoscopy results could be analyzed. A total of 25 subjects has normal fundus. Abnormalities were seen in 8 subjects, which comprised of 4 (12.1%) papilledema, 3 (9.1%) papillatrophy and 1 (3%) retinal haemorrhage. The highest mean aperture pressure was found in the papillatrophy group (37cmH2O), followed by papilledema (27cmH2O). Discussion: BI can increase intracranial pressure (ICP). Nonetheless, we found only 12.1% had papilledema. The highest mean aperture pressure was found in the papillatrophy group which showed an increase in ICP over a long period of time. The highest mortality was found in the normal fundus group so that papilledema cannot be directly associated with a worse prognosis considering the uncontrolled confounding factors such as other BI complications. Conclusion: Although BI causes an increase in ICP, papilledema is not always found. High aperture pressure was found in the papillatrophy group, but the highest mortality was found in the normal fundus group.

Analysis of the Asymmetry between Both Eyes in Early Diagnosis of Glaucoma Combining Features Extracted from Retinal Images and OCTs into Classification Models

This study aims to analyze the asymmetry between both eyes of the same patient for the early diagnosis of glaucoma. Two imaging modalities, retinal fundus images and optical coherence tomographies (OCTs), have been considered in order to compare their different capabilities for glaucoma detection. From retinal fundus images, the difference between cup/disc ratio and the width of the optic rim has been extracted. Analogously, the thickness of the retinal nerve fiber layer has been measured in spectral-domain optical coherence tomographies. These measurements have been considered as asymmetry characteristics between eyes in the modeling of decision trees and support vector machines for the classification of healthy and glaucoma patients. The main contribution of this work is indeed the use of different classification models with both imaging modalities to jointly exploit the strengths of each of these modalities for the same diagnostic purpose based on the asymmetry characteristics between the eyes of the patient. The results show that the optimized classification models provide better performance with OCT asymmetry features between both eyes (sensitivity 80.9%, specificity 88.2%, precision 66.7%, accuracy 86.5%) than with those extracted from retinographies, although a linear relationship has been found between certain asymmetry features extracted from both imaging modalities. Therefore, the resulting performance of the models based on asymmetry features proves their ability to differentiate healthy from glaucoma patients using those metrics. Models trained from fundus characteristics are a useful option as a glaucoma screening method in the healthy population, although with lower performance than those trained from the thickness of the peripapillary retinal nerve fiber layer. In both imaging modalities, the asymmetry of morphological characteristics can be used as a glaucoma indicator, as detailed in this work.

The application of artificial intelligence in glaucoma diagnosis and prediction.

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.

Takayasu's arteritis - a summary of current, clinically relevant knowledge of the disease

Takayasu's disease, also known as pulseless disease or aortic arch syndrome, is a rare inflammation of the aorta and its branches, and sometimes the pulmonary arteries, which, according to studies, occurs most frequently in people from the Far East and Asian countries. In addition, its incidence is significantly higher in women, and the mean age of affected patients is 45.4 years [1]. The first official clinical case report of the disease by Shimizu and Sano appeared in 1951, while the name of the disease was proposed by Cassamise and Okuda in 1954 to honour the first clinician, Mikito Takayasu, who as early as 1908 presented a case of a young female patient with characteristic fundus lesions associated with pulse disturbances [2].

Vitreoretinal Interface Changes After Anti-vascular Endothelial Growth Factor Treatment in Highly Myopic Eyes: A Real-World Study.

To investigate changes in the vitreoretinal interface after anti-vascular endothelial growth factor (anti-VEGF) treatment in highly myopic eyes. Eyes with myopic choroidal neovascularization (mCNV) treated with intravitreal injection of anti-VEGF in a single-center were retrospectively reviewed. Fundus abnormalities and features of optical computed tomography were studied. A total of 295 eyes from 254 patients were recruited to the study. Prevalence of myopic macular retinoschisis (MRS) was 25.4%, and the rates of progression and onset of MRS were 75.9% and 16.2%, respectively. Outer retinal schisis (β = 8.586, p = 0.003) and lamellar macular hole (LMH) (β = 5.015, p = 0.043) at baseline were identified risk factors for progression and onset of MRS, whereas male sex (β = 9.000, p = 0.039) and outer retinal schisis at baseline (β = 5.250, p = 0.010) were risk factors for MRS progression. Progression of MRS was first detected in outer retinal layers in 48.3% of eyes. Thirteen eyes required surgical intervention. Spontaneous improvements of MRS were observed in five eyes (6.3%). Changes in the vitreoretinal interface, such as progression, onset, and improvement of MRS, were observed after anti-VEGF treatment. Outer retinal schisis and LMH were risk factors of progression and onset of MRS after anti-VEGF treatment. Intravitreal injection of ranibizumab and retinal hemorrhage were protective factors for surgical intervention for vision-threatening MRS.