Search Results (120)

Background: Optical coherence tomography (OCT) is the main diagnostic technology used to detect damage to the retinal ganglion cells (RGCs) in glaucoma. However, it remains unclear which OCT parameter demonstrates the best diagnostic performance for eyes with early, especially preperimetric glaucoma (PPG). We determined the diagnostic performance of ganglion cell–inner plexiform layer (GCIPL) parameters using spectral-domain OCT (SD-OCT) in primary open-angle preperimetric and early perimetric glaucoma and compared them with optic nerve head (ONH) and peripapillary retinal nerve fiber layer (pRNFL) parameters. Methods: We analyzed 101 eyes: 36 normal eyes, 33 with PPG, and 32 with early perimetric glaucoma. All patients underwent Topcon SD–OCT imaging using the Optic Disc and Macular Vertical protocols. The diagnostic abilities of the GCIPL, rim area, vertical cup-to-disc ratio (CDR), and pRNFL were assessed using the area under the receiver operating characteristic curve (AUC). Results: For PPG, the AUCs ranged from 0.60 to 0.63 (GCIPL), 0.82 to 0.86 (ONH), and 0.49 to 0.75 (pRNFL). For early perimetric glaucoma, the AUCs for GCIPL and pRNFL ranged from 0.81 to 0.88 and 0.57 to 0.91, respectively, whereas both ONH parameters demonstrated an AUC of 0.89. The GCIPL parameters were significantly lower than both ONH parameters in detecting preperimetric glaucoma (p < 0.05). For early perimetric glaucoma, comparisons between the AUCs of the best-performing mGCIPL parameters and those of the best-performing pRNFL and ONH parameters revealed no significant differences in their diagnostic abilities (p > 0.05). Conclusions: GCIPL parameters exhibited a diagnostic performance comparable to that of ONH and pRNFL parameters for early perimetric glaucoma. However, their ability to detect preperimetric glaucoma was significantly lower than the ONH parameters. Full article

Databases play a crucial role in training, validating, and comparing AI models for detecting retinal diseases, as well as in clinical research, technology development, and healthcare professional training. Diabetic retinopathy (DR), a common diabetes complication, is a leading cause of vision impairment and blindness worldwide. Early detection and management are essential to prevent irreversible vision loss. Fundus photography, known for being economical and non-contact, is a widely applicable gold standard method that offers a convenient way to diagnose and grade DR. This paper presents a comprehensive review of 22 open-source fundus retinal image databases commonly used in DR research, highlighting their main characteristics and key features. Most of these datasets were released between 2000 and 2022. These databases are analyzed through an in-depth examination of their images, enabling objective comparison using color space distances and Principal Component Analysis (PCA) based on 16 key statistical features. Finally, this review aims to support informed decision-making for researchers and practitioners involved in DR diagnosis and management, ultimately improving patient outcomes. Full article

Underwater fish image segmentation is the key technology to realizing intelligent fisheries and ecological monitoring. However, the problems of light attenuation, blurred boundaries, and low contrast caused by complex underwater environments seriously restrict the segmentation accuracy. In this paper, RetinalConet, an underwater fish segmentation network based on bionic retina dual-channel and multi-module cooperation, is proposed. Firstly, the bionic retina dual-channel module is embedded in the encoder to simulate the separation and processing mechanism of light and dark signals by biological vision systems and enhance the feature extraction ability of fuzzy target contours and translucent tissues. Secondly, the dynamic prompt module is introduced, and the response of key features is enhanced by inputting adaptive prompt templates to suppress the noise interference of water bodies. Finally, the edge prior guidance mechanism is integrated into the decoder, and low-contrast boundary features are dynamically enhanced by conditional normalization. The experimental results show that RetinalCoNet is superior to other mainstream segmentation models in the key indicators of mDice, reaching 82.3%, and mIou, reaching 89.2%, and it is outstanding in boundary segmentation in many different scenes. This study achieves accurate fish segmentation in complex underwater environments and contributes to underwater ecological monitoring. Full article

Optical Coherence Tomography (OCT) has evolved from a breakthrough ophthalmologic imaging tool into a cornerstone technology in interventional cardiology. After its initial applications in retinal imaging in the early 1990s, OCT was subsequently envisioned for cardiovascular use. In 1995, its ability to visualize atherosclerotic plaques was demonstrated in an in vitro study, and the following year marked the acquisition of the first in vivo OCT image of a human coronary artery. A major milestone followed in 2000, with the first intracoronary imaging in a living patient using time-domain OCT. However, the real inflection point came in 2006 with the advent of frequency-domain OCT, which dramatically improved acquisition speed and image quality, enabling safe and routine imaging in the catheterization lab. With the advent of high-resolution, second-generation frequency-domain systems, OCT has become clinically practical and widely adopted in catheterization laboratories. OCT progressively entered interventional cardiology, first proving its safety and feasibility, then demonstrating superiority over angiography alone in guiding percutaneous coronary interventions and improving outcomes. Today, it plays a central role not only in clinical practice but also in cardiovascular research, enabling precise assessment of plaque biology and response to therapy. With the advent of artificial intelligence and hybrid imaging systems, OCT is now evolving into a true precision-medicine tool—one that not only guides today’s therapies but also opens new frontiers for discovery, with vast potential still waiting to be explored. Tracing its historical evolution from ophthalmology to cardiology, this narrative review highlights the key technological milestones, clinical insights, and future perspectives that position OCT as an indispensable modality in contemporary interventional cardiology. As a guiding thread, the myth of Prometheus is used to symbolize the evolution of OCT—from its illuminating beginnings in ophthalmology to its transformative role in cardiology—as a metaphor for how light, innovation, and knowledge can reveal what was once hidden and redefine clinical practice. Full article

Background: Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. Methods: This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. Results: The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. Conclusions: This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting. Full article

Purpose: This study aims to evaluate the clinical utility of a prototype ultra-widefield (UWF) single-capture optical coherence tomography (OCT) lens developed to image large areas of the retina. Material and Methods: This study included OCT and angio-OCT measurements performed with a REVO FC 130 (Optopol Technology, Poland) with an add-on widefield lens in a case series of 215 patients with retinal pathologies and 39 healthy subjects. The imaging width provided by the lens was 22 mm (covering a 110-degree field of view), while the scanning window height ranged from 2.8 to 6 mm. Results: The quality of the peripheral UWF OCT and angio-OCT images obtained by REVO FC 130 with the attachable lens is very good and sufficient for patient diagnosis, follow-up, and treatment planning. Both the boundaries of the non-perfusion zones and the location and extent of vascular proliferations can be accurately traced. Furthermore, the vitreoretinal interface can also be accurately assessed over a large area. The imaging quality of the macula with UWF OCT angiography is also good. The mean thickness measurement difference between a UWF and a standard 10 mm 3D retinal scan in a healthy individuals for the Central ETDRS sector was −1.37 ± 2.96 µm (the 95% limits of agreement (LoA) on Bland–Altman plots ranged from −6.82 to 2.43); for the Inferior Inner sector, it was −2.81 ± 1.09 µm (95% LoA, −4.94 to −0.68); for the Inferior Outer sector, it was −1.31 ± 2.58 µm (95% LoA, −6.38 to 3.75); for the Nasal Inner sector: −1.46 ± 1.19 µm (95% LoA, −3.79 to 0.88); for the Nasal Outer sector, it was −0.56 ± 2.61 µm (95% LoA, −5.67 to 4.55); for the Superior Inner sector, it was −2.71 ± 3.16 µm (95% LoA, −8.91 to 3.48); for the Superior Outer sector, it was −1.82 ± 1.39 µm (95% LoA, −4.55 to 0.91); for the Temporal Inner sector, it was −1.77 ± 2.24 µm (95% LoA, −6.16 to 2.62); for the Temporal Outer sector, it was −3.61 ± 1.43 µm (95% LoA, −6.41 to −0.81). Discussion: The proposed method of obtaining UWF OCT and UWF angio-OCT images using an add-on lens with the REVO FC 130 gives high-quality scans over the entire 110-degree field of view. This study also shows a high agreement of the ETDRS sector’s thickness measurements between UWF and standard retinal scans, which allows UWF to be used for quantitative macular thickness analysis. Considering its image quality, simplicity, and reliability, an add-on lens can be successfully used for the UWF OCT and OCT angiography evaluation of the retina on a daily basis. Full article

Introduction: Recent technological progress in optical imaging—such as adaptive optics, interferometry and tomography—has greatly improved the resolution of retinal imaging. The ability to capture sequential images over time is particularly valuable for continuous monitoring and assessment of retinal diseases. Methods: This cross-sectional study involved patients with type 2 diabetes mellitus and age-matched controls from the Diabetes and Ophthalmology Department of the Emergency Military Clinical Hospital “Dr. Constantin Papilian” Cluj-Napoca between October 2023 and October 2024. These patients were assessed for inclusion and exclusion criteria and then categorized into two groups: the diabetes group and control group. Each participant underwent a comprehensive ophthalmological examination and retinal evaluation using SS-OCT (Spectralis Heidelberg Engineering, Heidelberg, Germany). The parameters measured included the superficial and deep foveal avascular zones (FAZ) in only one eye for each patient, selected based on image quality. Additionally, each patient underwent quantitative analysis of serum C-reactive protein (CRP) levels. Results: A total of 33 patients (33 eyes) featured, 13 men and 20 women. The DM group showed statistically significant higher results for CRP value compared to healthy subjects (p < 0.001). Also, both superficial and deep FAZ areas were statistically significantly higher for diabetes patients compared to the healthy controls (p < 0.05). The correlation analysis revealed that there was no significant correlation between CRP and either superficial FAZ (p = 0.809) or deep FAZ (p = 0.659). However, a significant positive moderate correlation was found between superficial FAZ and deep FAZ (r = 0.577, p = 0.015). Conclusions: Our findings showed a significantly enlarged FAZ in diabetic patients compared to healthy individuals, highlighting its potential as an early indicator of microvascular alterations in diabetes. While CRP levels were notably elevated in the diabetic group, no significant association was found between CRP and FAZ measurements, suggesting that FAZ changes may occur independently of systemic inflammatory status. Full article

Background/Objectives: Diabetic retinopathy (DR) remains one of the primary causes of preventable vision impairment worldwide, particularly among individuals with long-standing diabetes. The progressive damage of retinal microvasculature can lead to irreversible blindness if not detected and managed at an early stage. Therefore, the development of reliable, non-invasive, and automated screening tools has become increasingly vital in modern ophthalmology. With the evolution of medical imaging technologies, Optical Coherence Tomography (OCT) has emerged as a valuable modality for capturing high-resolution cross-sectional images of retinal structures. In parallel, machine learning has shown considerable promise in supporting early disease recognition by uncovering complex and often imperceptible patterns in image data. Methods: This study introduces a novel framework for the early detection of DR through multifractal analysis of OCT images. Multifractal features, extracted using a box-counting approach, provide quantitative descriptors that reflect the structural irregularities of retinal tissue associated with pathological changes. Results: A comparative evaluation of several machine learning algorithms was conducted to assess classification performance. Among them, the Multi-Layer Perceptron (MLP) achieved the highest predictive accuracy, with a score of $98.02\%$, along with precision, recall, and F1-score values of $98.24\%$, $97.80\%$, and $98.01\%$, respectively. Conclusions: These results highlight the strength of combining OCT imaging with multifractal geometry and deep learning methods to build robust and scalable systems for DR screening. The proposed approach could contribute significantly to improving early diagnosis, clinical decision-making, and patient outcomes in diabetic eye care. Full article

Traumatic brain injury (TBI) induces complex molecular and cellular responses, often leading to vision deterioration and potential mortality. Current objective diagnostic methods are limited, necessitating the development of novel tools to assess disease severity. This review focuses on the retina, a readily approachable part of the central nervous system (CNS), as a potential indicator of TBI. We conduct a targeted database search and employ a blinded scoring system, incorporating both human and artificial intelligence (AI) assessments, to identify relevant articles. We then perform a detailed analysis to elucidate the molecular pathways and cellular changes in the retina following TBI. Recent findings highlight the involvement of key molecular markers, such as ionized calcium-binding adapter molecule 1 (IBA1), phosphorylated tau, glial fibrillary acidic protein (GFAP), and various cytokines (IL-1β, IL-6, and TNF). Additionally, the roles of oxidative stress, reactive oxygen species (ROS), and blood–retina barrier (BRB) disruption are explored. Based on these findings, we hypothesize that alterations in these molecular pathways and cellular components, particularly microglia, can serve as direct indicators of brain health and TBI severity. Recent technological advancements in retinal imaging now allow for a direct assessment of retinal cells, including microglia, and related inflammatory processes, facilitating the translation of these molecular findings into clinical practice. This review underscores the retina’s potential as a non-invasive window into the molecular pathophysiology of TBI. Full article

Alzheimer’s disease (AD) is a neurodegenerative disease that results in a loss of cognitive functions. The early discovery of it can potentially stop or decrease the severity of AD. Extensive research has been conducted to find AD biomarkers. In recent years, due to the development of AI technologies and the ease of obtaining retinal images, various machine learning (ML)- and deep learning (DL)-based methods of identifying AD patients from these images have been proposed. These models are significant as they represent a potential screening tool for AD and a tool for identifying biomarkers from retinal images. This paper reviews the recent progress in this direction. It presents an overview of relevant methods and analyzes their strengths and limitations. Also, it discusses common challenges and possible future directions related to this topic. Full article

Adaptive optics (AO)-corrected retina flood illumination imaging technology is widely used for investigating both structural and functional aspects of the retina. Given the inherent low-contrast nature of original retinal images, it is necessary to perform image restoration. Total variation (TV) regularization is an efficient regularization technique for AO retinal image restoration. However, a main shortcoming of TV regularization is its potential to experience the staircase effects, particularly in smooth regions of the image. To overcome the drawback, a new image restoration model is proposed for AO retinal images. This model utilizes the overlapping group sparse total variation (OGSTV) as a regularization term. Due to the structural characteristics of AO retinal images, only partial information regarding the PSF is known. Consequently, we have to solve a more complicated myopic deconvolution problem. To address this computational challenge, we propose an ADMM-MM-LAP method to solve the proposed model. First, we apply the alternating direction method of multiplier (ADMM) as the outer-layer optimization method. Then, appropriate algorithms are employed to solve the ADMM subproblems based on their inherent structures. Specifically, the majorization–minimization (MM) method is applied to handle the asymmetry OGSTV regularization component, while a modified version of the linearize and project (LAP) method is adopted to address the tightly coupled subproblem. Theoretically, we establish the complexity analysis of the proposed method. Numerical results demonstrate that the proposed model outperforms the existing state-of-the-art TV model across several metrics. Full article

Optical Coherence Tomography (OCT) has revolutionized the diagnosis and management of retinal diseases, offering high-resolution, cross-sectional imaging that aids in early detection and continuous monitoring. However, traditional OCT devices are limited to clinical settings and require a technician to operate, which poses accessibility challenges such as a lack of appointment availability, patient and family burden of frequent transportation, and heightened healthcare costs, especially when treatable pathology is undetected. With the increasing global burden of retinal conditions such as age-related macular degeneration (AMD) and diabetic retinopathy, there is a critical need for improved accessibility in the detection of retinal diseases. Advances in biomedical engineering have led to innovations such as portable models, community-based systems, and artificial intelligence-enabled image analysis. The SightSync OCT is a community-based, technician-free device designed to enhance accessibility while ensuring secure data transfer and high-quality imaging (6 × 6 mm resolution, 80,000 A-scans/s). With its compact design and potential for remote interpretation, SightSync widens the possibility for community-based screening for vision-threatening retinal diseases. By integrating innovations in OCT imaging, the future of monitoring for retinal disease can be transformed to reduce barriers to care and improve patient outcomes. This article discusses the evolution of OCT technology, its role in the diagnosis and management of retinal diseases, and how novel engineering solutions like SightSync OCT are transforming accessibility in retinal imaging. Full article

Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect different eye conditions early on. These conditions include age-related macular degeneration (AMD), diabetic retinopathy, cataracts, myopia, and glaucoma. Common eye conditions include cataracts, which cloud the lens and cause blurred vision, and glaucoma, which can cause vision loss due to damage to the optic nerve. The two conditions that could cause blindness if treatment is not received are age-related macular degeneration (AMD) and diabetic retinopathy, a side effect of diabetes that destroys the blood vessels in the retina. Problems include myopic macular degeneration, glaucoma, and retinal detachment—severe types of nearsightedness that are typically defined as having a refractive error of –5 diopters or higher—are also more likely to occur in people with high myopia. We intend to apply a user-friendly approach that will allow for faster and more efficient examinations. Our research attempts to streamline the eye examination procedure, making it simpler and more accessible than traditional hospital approaches. Our goal is to use deep learning and machine learning to develop an extremely accurate model that can assess medical images, such as eye retinal scans. This was accomplished by using a huge dataset to train the machine learning and deep learning model, as well as sophisticated image processing techniques to assist the algorithm in identifying patterns of various eye illnesses. Following training, we discovered that the CNN, VggNet, MobileNet, and hybrid Deep Learning models outperformed the SVM and Random Forest machine learning models in terms of accuracy, achieving above 98%. Therefore, our model could assist physicians in enhancing patient outcomes, raising survival rates, and creating more effective treatment plans for patients with these illnesses. Full article

Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools. Full article

Augmented reality (AR) technologies can improve the quality of life of individuals with visual impairments. The current study evaluated the efficacy of Retiplus, a new AR-based low-vision device, which was designed to enhance spatial awareness and visual function in patients with peripheral visual field loss. Thirteen patients diagnosed with retinitis pigmentosa (RP) participated in this study. The patients’ visual acuity, visual field, and subjective perception of peripheral vision and mobility were assessed both without and with the AR aid, following a training period consisting of five 1 h sessions. The results showed a significant expansion of the visual field (VF) in all four quadrants (right, left, upper, and lower) with a greater horizontal diameter enlargement (21.38° ± 12.94°) than vertical (15° ± 10.08°), with a statistically significant difference. However, the increase in VF was accompanied by a modest reduction in visual acuity due to the minification of the image on the display. Patient feedback also highlighted significant benefits on the ability to perform activities of daily living (ADL) in low-light environments and improved spatial orientation, suggesting that the AR system is helpful for some limitations imposed by patients’ conditions. These findings underscore the importance of optimizing AR technology to support visually impaired populations. Full article