Search Results (172)

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Predicting its progression is crucial for preventing late-stage AMD, as it is an irreversible retinal disease. Both genetic factors and retinal images are instrumental in diagnosing and predicting AMD progression. Previous studies have explored automated diagnosis using single fundus images and genetic variants, but they often fail to utilize the valuable longitudinal data from multiple visits. Longitudinal retinal images offer a dynamic view of disease progression, yet standard Long Short-Term Memory (LSTM) models assume consistent time intervals between training and testing, limiting their effectiveness in real-world settings. To address this limitation, we propose time-varied Long Short-Term Memory (TV-LSTM), which accommodates irregular time intervals in longitudinal data. Our innovative approach enables the integration of both longitudinal fundus images and AMD-associated genetic variants for more precise progression prediction. Our TV-LSTM model achieved an AUC-ROC of 0.9479 and an AUC-PR of 0.8591 for predicting late AMD within two years, using data from four visits with varying time intervals. Full article

With the growing use of deep learning in medical image analysis, automated classification of fundus images is crucial for the early detection of fundus diseases. However, the complexity of fluorescein fundus angiography (FFA) images poses challenges in the accurate identification of lesions. To address these issues, we propose the Enhanced Feature Fusion ConvNeXt (EFF-ConvNeXt) model, a novel architecture combining VGG16 and an enhanced ConvNeXt for FFA image classification. VGG16 is employed to extract edge features, while an improved ConvNeXt incorporates the Context-Aware Feature Fusion (CAFF) strategy to enhance global contextual understanding. CAFF integrates an Improved Global Context (IGC) module with multi-scale feature fusion to jointly capture local and global features. Furthermore, an SKNet module is used in the final stages to adaptively recalibrate channel-wise features. The model demonstrates improved classification accuracy and robustness, achieving 92.50% accuracy and 92.30% F1 score on the APTOS2023 dataset—surpassing the baseline ConvNeXt-T by 3.12% in accuracy and 4.01% in F1 score. These results highlight the model’s ability to better recognize complex disease features, providing significant support for more accurate diagnosis of fundus diseases. Full article

Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities. Full article

Background/Objectives: Senior-Loken syndrome (SLS) is a rare autosomal recessive renal–retinal disease caused by mutations in 10 genes. This study aimed to review the ophthalmic findings, renal function, and genotypes of Korean SLS cases. Methods: We retrospectively reviewed 17 genetically confirmed SLS patients in Korea, including 9 newly identified cases and 8 previously reported. Comprehensive ophthalmologic evaluations and renal assessments were conducted. Genetic testing was performed using whole-genome sequencing (WGS), whole-exome sequencing (WES), or Sanger sequencing. Results: Among the 17 patients, patients with NPHP1 mutations were most common (35.3%), followed by those with NPHP4 (29.4%), IQCB1 (NPHP5, 29.4%), and SDCCAG8 (NPHP10, 5.9%) mutations. Patients with NPHP1 mutations showed retinitis pigmentosa (RP) sine pigmento and preserved central vision independent of renal deterioration. Patients with NPHP4 mutations showed early renal dysfunction. Two patients aged under 20 maintained relatively good visual function, but older individuals progressed to severe retinopathy. Patients with IQCB1 mutations were generally prone to early and severe retinal degeneration, typically manifesting as Leber congenital amaurosis (LCA) (three patients), while two patients exhibited milder RP sine pigmento with preserved central vision. Notably, two out of five (40.0%) maintained normal renal function at the time of diagnosis, and both had large deletions in IQCB1. The patient with SDCCAG8 mutation exhibited both end-stage renal disease and congenital blindness due to LCA. Wide-field fundus autofluorescence (AF) revealed perifoveal and peripapillary hypoAF with a perifoveal hyperAF in younger patients across genotypes. Patients under 20 years old showed relatively preserved central vision, regardless of the underlying genetic mutation. Conclusions: The clinical manifestation of renal and ocular impairment demonstrated heterogeneity among Korean SLS patients according to causative genes, and the severity of renal dysfunction and visual decline was not correlated. Therefore, simultaneous comprehensive evaluations of both renal and ocular function should be performed at the initial diagnosis to guide timely intervention and optimize long-term outcomes. Full article

Fundus autofluorescence (FAF) is a non-invasive retinal imaging technique that helps visualize naturally occurring fluorophores, such as lipofuscin, and provides valuable insight into retinal diseases—particularly inherited retinal diseases (IRDs). FAF is especially useful in detecting subclinical or early-stage IRDs and in monitoring disease progression over time. In Stargardt disease, areas of decreased autofluorescence correlate with disease progression and have been proposed as a biomarker for future clinical trials. FAF can also help differentiate Stargardt disease from other macular dystrophies. In retinitis pigmentosa, hyperautofluorescent rings are a common feature on FAF and serve as an important marker for disease monitoring, especially as changes align with those seen on other imaging modalities. FAF is valuable in tracking progression of choroideremia and may help identify disease carrier status. FAF has also improved the characterization of mitochondrial retinopathies such as maternally inherited diabetes and deafness. As a rapid and widely accessible imaging modality, FAF plays a critical role in both diagnosis and longitudinal care of patients with IRDs. Full article

Glaucoma is an irreversible neurodegenerative disease that affects the optic nerve, leading to partial or complete vision loss. Early and accurate detection is crucial to prevent vision impairment, which necessitates the development of highly precise diagnostic tools. Deep learning (DL) has emerged as a promising approach for glaucoma diagnosis, where the model is trained on datasets of fundus images. To improve the detection accuracy, we propose a hybrid model for glaucoma detection that combines multiple DL models with two fine-tuning strategies and uses a majority voting scheme to determine the final prediction. In experiments, the hybrid model achieved a detection accuracy of 96.55%, a sensitivity of 98.84%, and a specificity of 94.32%. Integrating datasets was found to improve the performance compared to using them separately even with transfer learning. When compared to individual DL models, the hybrid model achieved a 20.69% improvement in accuracy compared to the best model when applied to a single dataset, a 13.22% improvement when applied with transfer learning across all datasets, and a 1.72% improvement when applied to all datasets. These results demonstrate the potential of hybrid DL models to detect glaucoma more accurately than individual models. Full article

Background: Syndromic inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of disorders, involving the retina and additional organs. Over 80 forms of syndromic IRD have been described. Methods: We aimed to phenotypically and genotypically characterize a cohort of 171 individuals from 140 Israeli families with syndromic IRD. Ophthalmic examination included best corrected visual acuity, fundus examination, visual field testing, retinal imaging and electrophysiological evaluation. Most participants were also evaluated by specialists in fields relevant to their extra-retinal symptoms. Genetic analyses included haplotype analysis, homozygosity mapping, Sanger sequencing and next-generation sequencing. Results: In total, 51% of the families in the cohort were consanguineous. The largest ethnic group was Muslim Arabs. The most common phenotype was Usher syndrome (USH). The most common causative gene was USH2A. In 29% of the families, genetic analysis led to a revised or modified clinical diagnosis. This included confirmation of an atypical USH diagnosis for individuals with late-onset retinitis pigmentosa (RP) and/or hearing loss (HL); diagnosis of Heimler syndrome in individuals with biallelic pathogenic variants in PEX6 and an original diagnosis of USH or nonsyndromic RP; and diagnosis of a mild form of Leber congenital amaurosis with early-onset deafness (LCAEOD) in an individual with a heterozygous pathogenic variant in TUBB4B and an original diagnosis of USH. Novel genotype–phenotype correlations included biallelic pathogenic variants in KATNIP, previously associated with Joubert syndrome (JBTS), in an individual who presented with kidney disease and IRD, but no other features of JBTS. Conclusions: Syndromic IRDs are a highly heterogeneous group of disorders. The rarity of some of these syndromes on one hand, and the co-occurrence of several syndromic and nonsyndromic conditions in some individuals, on the other hand, complicates the diagnostic process. Genetic analysis is the ultimate way to obtain an accurate clinical diagnosis in these individuals. Full article

In the past, only Oguchi disease was reported as a hereditary retinal disease from Japan. Dr. Chuuta Oguch was a Professor of Nagoya University in Japan. During the past 40 years, four new clinical entities in hereditary retinal disorders have been detected by the Miyake group from Nagoya, Japan. All disorders show essentially normal fundi, and the diagnosis was made mainly by the analysis of an electroretinogram (ERG). Gene mutations are detected in three of them. Bipolar cell (BP) dysfunction syndrome: Congenital stationary night blindness (CSNB) with negative ERG (a-wave is larger than b-wave) was named as the Schubert–Bornschein type in 1952 and considered to be an independent clinical entity. In 1986, Miyake group classified ninety patients with the Schubert–Bornschein type into two types (complete and incomplete type). The complete type of CSNB (CSNB1) showed no rod function, but the incomplete type CSNB (CSNB2) showed remaining rod function in both subjective dark adaptation and rod ERG. In order to investigate the pathogenesis, these two types of CSNB were analyzed by comparing the monkey ERGs using different glutamate analogs to the retina. The ERG analysis demonstrated that CSNB1 has a complete functional defect in the ON type BP, while CSNB2 has incomplete functional defects in the ON and OFF type BP in both rod and cone visual pathways. Evidence of several different genetic heterogeneities was reported in both diseases, indicating CSNB1 and CSNB2 are independent clinical entities. Another entity, showing total complete defect of both ON and OFF BP, was detected in 1974 and was reported by Miyake group in a brother and younger sister, showing severe photophobia, nystagmus, extremely low visual acuity, and disappearance of color vision (total color blindness). This disorder is a congenital stational condition, and subjective visual functions were severely deteriorated from birth but remained unchanged through life. This disease was termed “Total complete bipolar cell dysfunction syndrome (CSNB3)”. The relationship between BP and subjective visual function was unknown. These three kinds of BP diseases can provide information on how BP relates to subjective visual functions. Occult macular dystrophy (OMD): Occult macular dystrophy (OMD) was discovered by Miyake group in 1989. This disease shows an unusual, inherited macular dystrophy characterized by progressive decrease visual acuity due to macular dysfunction, but the fundus and fluorescein angiography are essentially normal. The full-field rod and cone ERG do not show any abnormality, but the focal macular ERG (FERG) or multifocal ERG is abnormal and the only method for diagnosis. Many pedigrees of this disorder suggest autosomal dominant heredity, showing a genetic mutation of RP1L1. This disease was termed “occult macular dystrophy”. “Occult” means “hidden from sight”. Recently, it has been called “Miyake disease”. Full article

Background: Methylmalonic acidemia combined with homocystinuria (cblC) can lead to infantile maculopathy. Although significant visual deterioration is commonly reported in early-onset cblC, we found poor awareness regarding formal assessments of ocular complications, especially in newborns, and of how these complications relate to the timing of therapy initiation. In this work, we present our experience and perform a literature review. Methods: We performed sequential fundus examinations, optical coherence tomography (OCT) and full-field electroretinography (ERG) under sedation following detection of signs of retinal degeneration. We also assessed visual fields using kinetic attraction perimetry. Results: We report a newborn who was referred on the eighth day of life, following a diagnosis of cblC through newborn screening (NBS), and who began treatment that same day. Close monitoring of retinal changes through fundus examinations allowed the detection of signs of retinal degeneration at 3 months, which progressed when checked at 5 months. At 7 months, OCT showed retinal thinning with the appearance of bull’s eye maculopathy in the corresponding region on fundoscopy; ERG revealed a reduction in the amplitude of both scotopic and photopic components, whereas kinetic attraction perimetry showed no abnormalities. Genetic investigation confirmed the disease, compound heterozygous for a nonsense variant in MMACHC and a splicing one in PRDX1. Conclusions: In cblC, retinal degeneration occurs in the first months of life despite timely treatment and adequate biochemical control, and it may manifest before any signs of visual deprivation appear. However, there is an early, narrow window during which therapy may slow down retinal degeneration enough to prevent sensory nystagmus. We recommend initiating therapy immediately after biochemical diagnosis, along with close ophthalmological monitoring, before the appearance of any signs. Full article

Eye diseases (EDs), including glaucoma, diabetic retinopathy, and cataracts, are major contributors to vision loss and reduced quality of life worldwide. These conditions not only affect millions of individuals but also impose a significant burden on global healthcare systems. As the population ages and lifestyle changes increase the prevalence of conditions like diabetes, the incidence of EDs is expected to rise, further straining diagnostic and treatment resources. Timely and accurate diagnosis is critical for effective management and prevention of vision loss, as early intervention can significantly slow disease progression and improve patient outcomes. However, traditional diagnostic methods rely heavily on manual analysis of fundus imaging, which is labor-intensive, time-consuming, and subject to human error. This underscores the urgent need for automated, efficient, and accurate diagnostic systems that can handle the growing demand while maintaining high diagnostic standards. Current approaches, while advancing, still face challenges such as inefficiency, susceptibility to errors, and limited ability to detect subtle retinal asymmetries, which are critical early indicators of disease. Effective solutions must address these issues while ensuring high accuracy, interpretability, and scalability. This research introduces a 2D single-channel convolutional neural network (CNN) based on ResNet101-V2 architecture. The model integrates gradient-weighted class activation mapping (Grad-CAM) to highlight retinal asymmetries linked to EDs, thereby enhancing interpretability and detection precision. Evaluated on retinal Optical Coherence Tomography (OCT) datasets for multiclass classification tasks, the model demonstrated exceptional performance, achieving accuracy rates of 99.90% for four-class tasks and 99.27% for eight-class tasks. By leveraging patterns of retinal symmetry and asymmetry, the proposed model improves early detection and simplifies the diagnostic workflow, offering a promising advancement in the field of automated eye disease diagnosis. Full article

Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis, disease progression, and surgical outcome prediction. Recent advances in artificial intelligence have enabled significant progress by extracting clinically relevant patterns from structural, functional, and molecular data. This review outlines the current applications of artificial intelligence in glaucoma care, including early detection using fundus photography and OCT and disease progression prediction using deep learning architectures such as convolutional neural networks, recurrent neural networks, transformer models, generative adversarial networks, and autoencoders. Surgical outcome forecasting has been enhanced through multimodal models that integrate electronic health records and imaging data. We also highlight emerging AI applications in omics analysis, including transcriptomics and metabolomics, for biomarker discovery and individualized risk stratification. Despite these advances, key challenges remain in interpretability, integration of heterogeneous data, and the lack of personalized surgical timing guidance. Future work should focus on transparent, generalizable, and multimodal AI models, supported by large, well-curated datasets, to advance precision medicine in glaucoma. Full article

Purpose: Dirofilariasis is a zoonotic infectious disease caused by a species belonging to the Dirofilaria genus. Human dirofilariasis cases have increased in Europe in the last few decades. Dogs and wild canids represent the definitive hosts and principal reservoirs of Dirofilaria repens, while mosquito species are biological vectors. Humans act as accidental hosts, and clinical manifestations depend on the location of the worm in the organs or tissues. We described the first case of ocular dirofilariasis in the Aosta Valley region (Italy). Case description: a 62-year-old Italian woman complained of recurrent ocular redness, pain and discomfort, accompanied by itching and foreign body sensation in the right eye. The slit lamp biomicroscopic examination revealed conjunctival congestion on the temporal region of bulbar conjunctiva, and a long whitish vermiform mobile mass was detected under the conjunctiva. The anterior chamber showed no flare or cells in either eye, and the dilated fundus examination was normal. The worm was immediately surgically removed to prevent further migration, and was diagnosed morphologically and molecularly as D. repens. Following surgical removal, the symptoms resolved completely and rapidly, with no recurrence of ocular symptoms recorded during 12-month follow-up visits. Conclusions: Ocular dirofilariasis can lead to misdiagnosis due to its rare ocular manifestations, and it is considered an emergent zoonosis in European countries. Accurate diagnosis and control of ocular dirofilariasis by D. repens require a multidisciplinary approach under the One Health framework to effectively address this emergent zoonosis. Full article

Glaucoma is a visual disease that affects millions of people, and early diagnosis can prevent total blindness. One way to diagnose the disease is through fundus image examination, which analyzes the optic disc and cup structures. However, screening programs in primary care are costly and unfeasible. Neural network models have been used to segment optic nerve structures, assisting physicians in this task and reducing fatigue. This work presents a methodology to enhance morphological biomarkers of the optic disc and cup in images obtained by a smartphone coupled to an ophthalmoscope through a deep neural network, which combines two backbones and a dual decoder approach to improve the segmentation of these structures, as well as a new way to combine the loss weights in the training process. The models obtained were numerically evaluated through Dice and IoU measures. The dice values obtained in the experiments reached a Dice of 95.92% and 85.30% for the optical disc and cup and an IoU of 92.22% and 75.68% for the optical disc and cup, respectively, in the BrG dataset. These findings indicate promising architectures in the fundus image segmentation task. Full article

Background/Objectives: Retinal vein occlusion (RVO) is a major cause of vision loss globally. Although magnesium (Mg) is crucial for vascular health, its association with RVO risk is unknown. Thus, we aimed to further examine this association. Methods: This cross-sectional study included participants of the Korean National Health and Nutrition Examination Survey 2017–2021 aged ≥19 years (n = 16,358). RVO diagnosis was based on fundus imaging or was self-reported. Based on their daily Mg intake, we categorized participants into low (<120 mg), intermediate (men: 120–300 mg; women: 120–400 mg), and sufficient (men: ≥300 mg; women: ≥400 mg) intake groups and compared their characteristics across groups. Results: RVO prevalence was 0.7%. Compared to the non-RVO group, the RVO group was characterized by older individuals, fewer current alcohol consumers, a higher prevalence of hypertension and chronic kidney disease, and a lower intake of fiber, iron, calcium, vitamin E, and Mg. After full adjustment, sufficient Mg intake was significantly associated with a 64% reduced risk of RVO (odds ratio [OR] 0.36, 95% confidence interval [CI] 0.18–0.71, p = 0.003). This association was particularly notable among individuals aged 19–59 years (OR 0.18, 95% CI 0.04–0.82, p = 0.027), those with hypertension (OR 0.29, 95% CI 0.13–0.67, p = 0.003), and those without glaucoma (OR 0.33, 95% CI 0.15–0.71, p = 0.004). Conclusions: Sufficient Mg intake may reduce RVO risk among adults aged <60 years, individuals with hypertension, and those without glaucoma. Further research should validate the benefits of Mg supplementation in preventing RVO. Full article