Search Results (80)

Background: This case–control study evaluated the concentrations of zinc (Zn), iron (Fe), and copper (Cu) in the aqueous humor (AH) of patients with primary glaucoma, and their relationships with clinical factors. Methods: This study enrolled 100 patients with primary glaucoma and categorized them into subtypes: acute angle-closure crisis (AACC), primary angle-closure glaucoma (PACG), and primary open-angle glaucoma (POAG). A total of 67 patients with senile cataract were enrolled as controls. Their AH samples and clinical information were obtained. Results: In primary glaucoma, Zn, Fe, and Cu concentrations increased, especially in AACC group; Zn, Fe, and Cu were positively correlated mutually; and decreased Zn/Fe and increased Fe/Cu were observed. The number of quadrants with closed anterior chamber angle on gonioscopy was positively associated with Fe and Cu levels in AACC and with Zn and Cu levels in PACG. In POAG, we found negative associations between Zn and the number of quadrants with retinal nerve fiber layer thinning on optical coherence tomography, Fe and age, and Cu and the cup-to-disc ratio. Trace metals showed high efficiency in discriminating primary glaucoma from controls. Conclusions: Zn, Fe, and Cu concentrations in patients with primary glaucoma increased and were associated with clinical factors, acting as potential biomarkers. Full article

Segmentation of the Optic Disc (OD) and Optic Cup (OC) boundaries in fundus images is a critical step for early glaucoma diagnosis, but accurate segmentation is challenging due to low boundary contrast and significant anatomical variability. To address these challenges, this study proposes a novel segmentation framework that integrates structure-preserving data augmentation, Boundary-aware Transformer Attention (BAT), and Geometry-aware Loss. We enhance data diversity while preserving vascular and tissue structures through truncated Gaussian-based sampling and colormap transformations. BAT strengthens boundary recognition by globally learning the inclusion relationship between the OD and OC within the skip connection paths of U-Net. Additionally, Geometry-aware Loss, which combines the normalized Hausdorff Distance with the Dice Loss, reduces fine-grained boundary errors and improves boundary precision. The proposed model outperforms existing state-of-the-art models across five public datasets—DRIONS-DB, Drishti-GS, REFUGE, G1020, and ORIGA—and achieves Dice scores of 0.9127 on Drishti-GS and 0.9014 on REFUGE for OC segmentation. For joint segmentation of the OD and OC, it attains high Dice scores of 0.9892 on REFUGE, 0.9782 on G1020, and 0.9879 on ORIGA. Ablation studies validate the independent contributions of each component and demonstrate their synergistic effect when combined. Furthermore, the proposed model more accurately captures the relative size and spatial alignment of the OD and OC and produces smooth and consistent boundary predictions in clinically significant regions such as the region of interest (ROI). These results support the clinical applicability of the proposed method in medical image analysis tasks requiring precise, boundary-focused segmentation. Full article

Accurate glaucoma diagnosis relies on precise segmentation of the optic disc (OD) and optic cup (OC) in retinal images. However, despite the development of numerous automatic segmentation models, the lack of annotations in the target domain and domain shift among datasets continue to limit their segmentation performance. To address these issues, we propose a Causal Self-Supervised Network (CSSN) that leverages self-supervised learning to enhance model performance. First, we construct a Structural Causal Model (SCM) and employ backdoor adjustment to convert the conventional conditional distribution into an interventional distribution, effectively severing the influence of style information on feature extraction and pseudo-label generation. Subsequently, the low-frequency components of source and target domain images are exchanged via Fourier transform to simulate cross-domain style transfer. The original target images and their style-transferred counterparts are then processed by a dual-path segmentation network to extract their respective features, and a confidence-based pseudo-label fusion strategy is employed to generate more reliable pseudo-labels for self-supervised learning. In addition, we employ adversarial training and cross-domain contrastive learning to further reduce style discrepancies between domains. The former aligns feature distributions across domains using a feature discriminator, effectively mitigating the adverse effects of style inconsistency, while the latter minimizes the feature distance between original and style-transferred images, thereby ensuring structural consistency. Experimental results demonstrate that our method achieves more accurate OD and OC segmentation in the target domain during testing, thereby confirming its efficacy in cross-domain adaptation tasks. 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

Coffee is one of the most widely consumed beverages globally, with Americans averaging 3.1 cups per day. However, before coffee beans can be brewed into a drinkable form, they undergo several critical stages, including harvesting, processing, roasting, grinding, and extraction. During the processing and roasting phases, a significant challenge arises: stones that are similar in size and shape to coffee beans can inadvertently mix into the batch. These stones are difficult to detect using conventional methods, and their presence can have severe consequences. When stones are ground alongside coffee beans, they can cause significant damage to the grinder’s burrs. Commercial coffee grinders typically employ conical or flat burrs, which consist of two circular discs or an inner blade and a disc. These burrs undergo specialized heat treatment and surface processing to ensure durability and precision, making them highly expensive components. Replacing damaged burrs is not only costly but also requires meticulous calibration of the parallelism between the inner blade and the disc to maintain grinding quality. The introduction of stones into the grinding process can lead to equipment damage, resulting in operational downtime and financial losses. To address this issue, this paper proposes a novel method based on Empirical Mode Decomposition (EMD) for detecting stones in coffee beans. The approach analyzes the acoustic wave patterns generated when stones impact or rotate within the grinder. Full article

Childhood glaucoma encompasses a group of rare but severe ocular disorders characterized by increased intraocular pressure (IOP), posing significant risks to vision and quality of life. Primary congenital glaucoma has a prevalence of one in 10,000–68,000 people in Western countries. More worryingly, it is responsible for 5–18% of all childhood blindness cases. According to the Childhood Glaucoma Research Network (CGRN), this spectrum of disease is classified into primary glaucoma (primary congenital glaucoma and juvenile open-angle glaucoma) and secondary glaucomas (associated with non-acquired ocular anomalies, non-acquired systemic disease, acquired conditions, and glaucoma after cataract surgery). They present very specific ocular characteristics, such as buphthalmos or progressive myopic shift, corneal modifications such as Haab striae, corneal edema or increased corneal diameter, and also glaucoma findings including high intraocular pressure, specific visual fields abnormalities, and optic nerve damage such as increased cup-disc ratio, cup-disc ratio asymmetry of at least 0.2 and focal rim thinning. Surgical intervention remains the cornerstone of treatment, and initial surgical options include angle surgeries such as goniotomy and trabeculotomy, aimed at improving aqueous outflow. For refractory cases, trabeculectomy and glaucoma drainage devices (GDDs) serve as second-line therapies. Advanced cases may require cyclodestructive procedures, including transscleral cyclophotocoagulation, reserved for eyes with limited visual potential. All in all, with appropriate management, the prognosis of PCG may be quite favorable: stationary disease has been reported in 90.3% of cases after one year, with a median visual acuity in the better eye of 20/30. Immediate recognition of the specific signs and symptoms by caregivers, primary care providers, and ophthalmologists, followed by prompt diagnosis, comprehensive surgical planning, and involving the caregivers in the follow-up schedule remain critical for optimizing outcomes in childhood glaucoma management. Full article

Biofilms are bacterial communities on surfaces within an extracellular matrix. Targeting biofilm-specific bacteria is crucial, and natural compounds with reported antibiofilm activity have garnered significant interest. The study evaluated the antibacterial and antibiofilm activity of Erythrina senegalensis leaf extract against multidrug-resistant (MDR) Gram-negative bacteria, including Salmonella Typhimurium, S. Typhi, S. Enteritidis, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The leaf extract was prepared using aqueous and ethanol solvents, and qualitative phytochemical screening revealed the presence of various bioactive compounds such as tannins, saponins, cardiac glycosides, flavonoids, terpenoids, alkaloids, anthraquinone, reducing sugar, and ketones. A Kirby–Bauer disc diffusion assay was performed to test the susceptibility of antibiotics, and the antibacterial efficacy of the aqueous and ethanol extracts of E. senegalensis was determined using the cup-plate method, while the antibiofilm activities were determined using the crystal violet titer-plate method. The aqueous and ethanol extracts of E. senegalensis revealed the presence of tannins, saponins, cardiac glycosides, flavonoids, terpenoids, alkaloids, anthraquinone, reducing sugar, and ketones. The study found that the Gram-negative bacteria isolates that were MDR were S. Typhimurium, S. Enteritidis, and P. aeruginosa, while K. pneumoniae was resistant to beta-lactam and fluoroquinolones, and S. Typhi was susceptible to all antibiotics tested. Statistically, susceptibility to antibiotics had an inverse, weak, and significant relationship with biofilm production (r = −0.453, −0.106, −0.124, −0.106, −0.018, n = 10, p < 0.05). The aqueous extract showed good biofilm inhibition against K. pneumoniae and P. aeruginosa, and poor biofilm inhibition against S. Enteritidis, while S. Typhimurium and S. Typhi exhibited no biofilm inhibition. The ethanol extract did not demonstrate any antibiofilm activity against the tested Gram-negative pathogens. The study suggests that the Gram-negative bacteria’s capacity to form biofilms is negatively associated with their antibiotic resistance phenotypes, and the aqueous extract of E. senegalensis exhibited moderate antibiofilm activity against K. pneumoniae, P. aeruginosa, and S. Enteritidis. Full article

Most existing optic disc (OD) and cup (OC) segmentation models are biased to the dominant size and easy class (normal class), resulting in suboptimal performances on glaucoma-confirmed samples. Thus, these models are not optimal choices for assisting in tracking glaucoma progression and prognosis. Moreover, fully supervised models employing annotated glaucoma samples can achieve superior performances, although restricted by the high cost of collecting and annotating the glaucoma samples. Therefore, in this paper, we are dedicated to developing a glaucoma-specialized model by exploiting low-cost annotated normal fundus images, simultaneously adapting various common scenarios in clinical practice. We employ a contrastive learning and domain adaptation-based model by exploiting shared knowledge from normal samples. To capture glaucoma-related features, we utilize a Gram matrix to encode style information and the domain adaptation strategy to encode domain information, followed by narrowing the style and domain gaps between normal and glaucoma samples by contrastive and adversarial learning, respectively. To validate the efficacy of our proposed model, we conducted experiments utilizing two public datasets to mimic various common scenarios. The results demonstrate the superior performance of our proposed model across multi-scenarios, showcasing its proficiency in both the segmentation- and glaucoma-related metrics. In summary, our study illustrates a concerted effort to target confirmed glaucoma samples, mitigating the inherent bias issue in most existing models. Moreover, we propose an annotation-efficient strategy that exploits low-cost, normal-labeled fundus samples, mitigating the economic- and labor-related burdens by employing a fully supervised strategy. Simultaneously, our approach demonstrates its adaptability across various scenarios, highlighting its potential utility in both assisting in the monitoring of glaucoma progression and assessing glaucoma prognosis. Full article

Objective: The study aimed to evaluate the interobserver agreement among glaucoma subspecialists in diagnosing glaucoma and to explore the causes of diagnostic discrepancies. Methods: Three experienced glaucoma subspecialists independently assessed frequency domain optical coherence tomography, fundus color photographs, and static perimetry results from 464 eyes of 275 participants, adhering to unified glaucoma diagnostic criteria. All data were collected from the Wenzhou Glaucoma Progression Study between August 2014 and June 2021. Results: The overall interobserver agreement among the three experts was poor, with a Fleiss’ kappa value of 0.149. The kappa values interobserver agreement between pairs of experts ranged from 0.133 to 0.282. In 50 cases, or approximately 10.8%, the three experts reached completely different diagnoses. Agreement was more likely in cases involving larger average cup-to-disc ratios, greater vertical cup-to-disc ratios, more severe visual field defects, and thicker retinal nerve fiber layer measurements, particularly in the temporal and inferior quadrants. High myopia also negatively impacted interobserver agreement. Conclusions: Despite using unified diagnostic criteria for glaucoma, significant differences in interobserver consistency persist among glaucoma subspecialists. To improve interobserver agreement, it is recommended to provide additional training on standardized diagnostic criteria. Furthermore, for cases with inconsistent diagnoses, long-term follow-up is essential to confirm the diagnosis of glaucoma. Full article

Background/Objectives: The aim of this study was to identify the most reliable ocular exam and establish a threshold for deciding whether to perform neuroimaging in order to screen for diverse diseases other than normal-tension glaucoma (NTG). A retrospective, observational, comparative study was used. Methods: In total, 106 individuals with atypical features of NTG who underwent glaucoma assessments and contrast-enhanced MRI of the brain or orbit were included. The criteria for atypical NTG included the following: (1) unilateral normal-tension glaucoma, (2) visual field (VF) damage inconsistent with optic disc appearance, (3) fast VF progression, (4) worsening of visual acuity, (5) optic disc pallor, (6) scotoma restricted by a vertical line, and (7) central scotoma. Glaucoma evaluations included measurements of visual acuity, intraocular pressure, central corneal thickness, axial length, cup–disc ratio, retinal nerve fiber layer (RNFL) thickness, ganglion cell–inner plexiform layer (GCIPL) thickness, mean deviation (MD), and visual field index (VFI). Statistical analyses involved independent t-tests, receiver operating characteristic (ROC) curves, and area under the curve (AUC) in order to differentiate neuro-ophthalmological conditions from NTG, compare the diagnostic power of each factor, and determine the cut-off value. Results: Relatively fewer diagnoses of non-glaucomatous diseases were associated with unilateral NTG, the worsening of VA, and central scotoma. Factors such as rapid visual field progression, optic disc pallor, and scotoma restricted by a vertical line had a relatively higher diagnostic rate of non-glaucomatous diseases. There were significant differences in average RNFL and GCIPL thicknesses at the nasal quadrant between NTG and NTG-mimicking conditions. Only the GCIPL thickness at the nasal quadrant had reliable power for discriminating between neuro-ophthalmological disease and NTG. For the GCIPL thickness at the nasal quadrant, the AUC was 0.659, and the cut-off value was 65.75. Conclusions: When deciding whether to proceed with imaging, such as carrying out an MRI test, for NTG patients with atypical NTG characteristics, it would be advisable to consider the nasal sector cut-off value of GCIPL thickness. Full article

(1) Objective: This study investigated the relationship between long-term particulate matter (PM_2.5) exposure and optic disc parameters—vertical cup-to-disc ratio (vCDR), vertical optic disc diameter (vDD), and vertical optic cup diameter (vCD)—in patients with type 2 diabetes mellitus (T2DM). (2) Methods: A cross-sectional analysis was conducted using data from 65,750 T2DM patients in the 2017–2018 Shanghai Cohort Study of Diabetic Eye Disease (SCODE). Optic disc parameters were extracted from fundus images, and PM_2.5 exposure was estimated using a random forest model incorporating satellite and meteorological data. Multivariate linear regression models were applied, adjusting for confounders including age, gender, body mass index, blood pressure, glucose, time of T2DM duration, smoking, drinking, and physical exercise. (3) Results: A 10 μg/m³ increase in PM_2.5 exposure was associated with significant reductions in vCDR (−0.008), vDD (−42.547 μm), and vCD (−30.517 μm) (all p-values < 0.001). These associations persisted after sensitivity analyses and adjustments for other pollutants like O₃ and NO₂. (4) Conclusions: Long-term PM_2.5 exposure is associated with detrimental changes in optic disc parameters in patients with T2DM, suggesting possible optic nerve atrophy. Considering the close relationship between the optic nerve and the central nervous system, these findings may also reflect broader neurodegenerative processes. Full article

Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup. Full article

Purpose: To assess spatial contrast sensitivity (CS) in suspected primary open-angle glaucoma (POAG) patients. Methods: CS was measured using sinusoidal gratings of 4 cycles/degree. First, foveal and peripheral CS were assessed in 34 suspected POAG patients and compared with 71 and 28 age-matched healthy individuals for foveal and peripheral conditions, respectively. Second, foveal CS was assessed in 34 early POAG patients age-matched with suspected POAG patients. Analyses were performed considering two age ranges: Under and Over 50 y.o. Correlations were evaluated between CS and clinical parameters. Diagnostic accuracy was also analyzed. Results: Peripheral CS was lower in older suspected POAG patients (23.4 ± 16.1) than the control group (39.1 ± 28.2) (p = 0.040). Foveal CS was reduced in suspected POAG participants (Under 50: 146.8 ± 63.3; p = 0.004. Over 50: 110.5 ± 65.0; p = 0.044) and in early POAG patients (Under 50: 141.2 ± 72.6; p = 0.002. Over 50: 80.2 ± 54.5 p < 0.001), both compared to the control group (Under 50: 213.5 ± 66.2. Over 50: 138.6 ± 71.7). CS was lower in early POAG than in POAG suspected in older patients (p = 0.042). Foveal CS was correlated with age (Early: p = 0.001. Suspect: p = 0.002) and with the cup–disc ratio only in early POAG patients (p < 0.001). Foveal CS had fair (AUC = 0.74) diagnostic accuracy for early POAG patients. Conclusions: CS in suspected POAG patients is lower than in healthy individuals. Our findings evidence the spatial vision loss before the onset of POAG. Full article

Glaucoma, a leading cause of permanent blindness worldwide, necessitates early detection to prevent vision loss, a task that is challenging and time-consuming when performed manually. This study proposes an automatic glaucoma detection method on enhanced retinal images using deep learning. The system analyzes retinal images, generating masks for the optic disc and optic cup, and providing a classification for glaucoma diagnosis. We employ a U-Net architecture with a pretrained residual neural network (ResNet34) for segmentation and an EfficientNetB0 for classification. The proposed framework is tested on publicly available datasets, including ORIGA, REFUGE, RIM-ONE DL, and HRF. Our work evaluated the U-Net model with five pretrained backbones (ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0) and examined preprocessing effects. We optimized model training with limited data using transfer learning and data augmentation techniques. The segmentation model achieves a mean intersection over union (mIoU) value of 0.98. The classification model shows remarkable performance with 99.9% training and 100% testing accuracy on ORIGA, 99.9% training and 99% testing accuracy on RIM-ONE DL, and 98% training and 100% testing accuracy on HRF. The proposed model outperforms related works and demonstrates potential for accurate glaucoma classification and detection tasks. Full article

Glaucoma, a predominant cause of visual impairment on a global scale, poses notable challenges in diagnosis owing to its initially asymptomatic presentation. Early identification is vital to prevent irreversible vision impairment. Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. Nevertheless, limited approaches have been suggested to improve glaucoma classification due to issues like inadequate training data, variations in feature distribution, and the overall quality of samples. Furthermore, fundus images display significant similarities and slight discrepancies in lesion sizes, complicating glaucoma classification when utilizing ViTs. To address these obstacles, we introduce the contour-guided and augmented vision transformer (CA-ViT) for enhanced glaucoma classification using fundus images. We employ a Conditional Variational Generative Adversarial Network (CVGAN) to enhance and diversify the training dataset by incorporating conditional sample generation and reconstruction. Subsequently, a contour-guided approach is integrated to offer crucial insights into the disease, particularly concerning the optic disc and optic cup regions. Both the original images and extracted contours are given to the ViT backbone; then, feature alignment is performed with a weighted cross-entropy loss. Finally, in the inference phase, the ViT backbone, trained on the original fundus images and augmented data, is used for multi-class glaucoma categorization. By utilizing the Standardized Multi-Channel Dataset for Glaucoma (SMDG), which encompasses various datasets (e.g., EYEPACS, DRISHTI-GS, RIM-ONE, REFUGE), we conducted thorough testing. The results indicate that the proposed CA-ViT model significantly outperforms current methods, achieving a precision of 93.0%, a recall of 93.08%, an F1 score of 92.9%, and an accuracy of 93.0%. Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks. Full article