Search Results (57)

Background: In clinical practice, visual outcomes with anti-VEGF therapy may be worse than those observed in clinical trials. In this study, we aim to investigate the long-term outcomes of neovascularization treated with intravitreal aflibercept injections (IAI) in a teaching hospital setting. Methods: This is a retrospective, single-center study including 81 nAMD patients (116 eyes), those both newly diagnosed and switched from ranibizumab. All patients had a follow-up duration of at least seven years. Treatment involved three monthly injections followed by either a pro re nata (PRN) or treat and extend regimen. Follow-up care was primarily conducted by training physicians. The primary endpoint was the change in best-corrected visual acuity (BCVA) over seven years. Secondary endpoints included central retinal thickness changes, qualitative OCT parameters, macular atrophy progression, injection frequency, and treatment adherence. Results: Among the 116 eyes, 52 (44.8%) completed the seven-year follow-up. Visual acuity improved by +2.1 letters in the overall population (+6.3 letters in treatment-naive eyes) after the loading phase but gradually declined, resulting in a loss of −12.3 letters at seven years. BCVA remained stable (a loss of fewer than 15 letters) in 57.7% of eyes. Central retinal thickness (CRT) decreased significantly during follow-up in both naive and switcher eyes. Macular atrophy occurred in 94.2% of eyes, progressing from 1.42 mm² to 8.55 mm² over seven years (p < 0.001). The mean number of injections was 4.1 ± 1.8 during the first year and 3.7 per year thereafter. Advanced age at diagnosis was a risk factor for loss to follow-up, with bilaterality being a protective factor against loss to follow-up (p < 0.05). Conclusions: This study highlights the challenges faced by a retina clinic in a teaching hospital. Suboptimal functional and anatomical outcomes in real life may derive from insufficient patient information and inconsistent monitoring, which contributes to undertreatment and affects long-term visual outcomes. It also raises concerns about supervision in a teaching hospital which needs to be improved. Full article

Visual field (VF) testing remains a cornerstone in assessing retinal function by measuring how well different parts of the retina detect light. It is essential for early detection, monitoring, and management of many retinal diseases. By mapping retinal sensitivity, VF exams can reveal functional loss before structural changes become visible. This review summarizes how VF testing is applied across key conditions: hydroxychloroquine (HCQ) retinopathy, age-related macular degeneration (AMD), diabetic retinopathy (DR) and macular edema (DME), and inherited disorders including inherited dystrophies such as retinitis pigmentosa (RP). Traditional methods like the Goldmann kinetic perimetry and simple tools such as the Amsler grid help identify large or central VF defects. Automated perimetry (e.g., Humphrey Field Analyzer) provides detailed, quantitative data critical for detecting subtle paracentral scotomas in HCQ retinopathy and central vision loss in AMD. Frequency-doubling technology (FDT) reveals early neural deficits in DR before blood vessel changes appear. Microperimetry offers precise, localized sensitivity maps for macular diseases. Despite its value, VF testing faces challenges including patient fatigue, variability in responses, and interpretation of unreliable results. Recent advances in artificial intelligence, virtual reality perimetry, and home-based perimetry systems are improving test accuracy, accessibility, and patient engagement. Integrating VF exams with these emerging technologies promises more personalized care, earlier intervention, and better long-term outcomes for patients with retinal disease. Full article

Background/Objectives: This study evaluates whether ultrawide-field optical coherence tomography angiography (UWF-OCTA) can guide navigated laser therapy for non-perfused areas (NPAs) in branch retinal vein occlusion (BRVO). It further explores whether the laser spots can be accurately placed according to plan, considering that the retina is three-dimensional (3D), while UWF-OCTA provides two-dimensional (2D) images. Methods: UWF-OCTA images from three devices—VG200, Xephilio OCT-S1, and Bmizar—guided the treatments. These images were superimposed onto NAVILAS^® system images to guide NPA treatments. Pre-treatment planning was strategically designed to avoid normal and collateral vessels, with immediate post-laser OCTA and en face images assessing the efficacy of the laser spots in avoiding these vessels as planned. The accuracy of navigated laser therapy was further analyzed by comparing the intended laser locations with the actual spots. Results: All montaged OCTA images from the three devices were seamlessly integrated into the navigated laser system without registration errors. All patients received treatments targeting the NPAs as planned. However, not all collateral or normal vessels were successfully avoided by the laser spots. A further analysis revealed that the actual locations of the laser spots deviated slightly from the planned locations, particularly in the mid-periphery areas. Conclusions: UWF-OCTA-guided navigated laser photocoagulation is feasible and precise for treating NPAs in BRVO. Nonetheless, minor deviations between planned and actual locations were observed. This discrepancy, particularly important when treating diseases of the macular area, should be carefully considered when employing OCTA-guided navigated laser photocoagulation. Full article

Adaptive optics scanning laser ophthalmoscopy optoretinography quantifies cellular function in the living retina by measuring the en face intensity change in cone photoreceptors due to visual stimulation. To fulfill the potential of optoretinography as a biomarker for assessing function in disease, we require normative optoretinographic measurements across the retina. Here we provide such measurements. We use a custom adaptive optics scanning laser ophthalmoscope to investigate cone optoretinogram (ORG) amplitudes across retinal eccentricity in five normal-sighted participants. For this purpose, we aggregated signals across cones in each measurement (~1° by 1° patch) to provide a measurement we call the population ORG. Average population ORG amplitudes decreased with increasing eccentricity for all participants, although there were individual differences in the detailed pattern of the decrease. ORG amplitudes were correlated with the thickness of the outer retina as measured using clinical OCT, which also decreases with eccentricity. Characterizing the population cone ORG as a function of eccentricity in normal-sighted participants is an important step towards establishing norms that will allow it to be used as a biomarker for assessing photoreceptor function in retinal disease. Full article

Background: Virtual clinics (VCs) have proven to be an effective solution for the increasing strain on Medical Retina (MR) services, although imaging quality issues (IQIs) persist. Our aim was to compare the quality of two ultra-wide-field (UWF) imaging modalities (Optos and Clarus) in real-world MR-VC settings. Methods: We conducted a real-world, prospective study. Data were collected from 6 Moorfields NHS Trust MR-VCs between September and October 2024. We obtained patient demographics and characteristics, primary diagnosis, UWF imaging types and images obtained, and follow-up outcomes. Results: Optos (California RG/RGB, and Monaco) was used for 56.7% (n = 152) and Zeiss Clarus 500 for 43.3% (n = 116) of the total cohort (n = 268). No statistically significant difference (p = 0.14) was found between the two in terms of the rates of IQIs. FAF (p = 0.001) acquisition was significantly higher when Optos was used. Of the patients affected by IQIs, 10 were examined in a face-to-face clinic (F2FC). No difference in IQI rates was observed when pathology-related poor image quality was considered (p = 0.561). A significantly (p = 0.001) higher rate of F2F follow-ups was found for red-flag pathologies and unexplained vision loss, with a statistically significantly higher rate of virtual follow-ups for non-red-flag pathologies (p = 0.001). Conclusions: A total of 7.5% of the clinical decisions were impacted by IQIs; 11.1% of F2FC follow-ups. Neither UWF imaging modality type was inferior in terms of IQI rates. FAF acquisition was higher with Optos, likely representing greater user-dependency for Clarus. The outcomes were not influenced by FAF acquisition, indicating that routine acquisition is not required in MR-VCs and instead should be obtained when clinically required. Full article

For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface’s weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications. Full article

Background/Objectives: Neurodegenerative ocular diseases, such as age-related macular degeneration (AMD) and glaucoma, represent growing public health concerns. Oxidative stress plays a key role in their development, damaging retinal cells and accelerating disease progression. Melatonin (Mel) is a potent antioxidant with neuroprotective properties; however, it faces limitations such as low solubility. This study proposes the use of human serum albumin nanoparticles (Np-HSA) to enhance the delivery of Mel to the posterior segment of the eye and evaluates its neuroprotective and anti-apoptotic effects on the retina. Methods: A model of retinal degeneration was induced in New Zealand albino rabbits using cytotoxic and oxidative agents. Np-HSA-Mel nanoparticles were administered subconjunctivally, and cellular viability and retinal functionality were assessed using flow cytometry and pupillary light reflex (PLR). Histological and immunohistochemical studies, including the TUNEL assay, were performed to analyse cell survival and apoptotic index. Results: Np-HSA-Mel significantly preserved pupillary function and cell viability, demonstrating lower apoptosis compared to Mel solution and Np-HSA alone. Histologically, eyes treated with Np-HSA-Mel exhibited fewer structural alterations and greater cellular organisation. The TUNEL assay confirmed a significant reduction in the apoptotic index of retinal ganglion cells (RGCs) treated with Np-HSA-Mel. Conclusions: Np-HSA-Mel effectively overcame ocular barriers, achieving greater neuroprotective efficacy at the retinal level. These findings highlight the synergistic potential of albumin and Mel in treating neurodegenerative ocular diseases, opening new perspectives for future therapies. Full article

The eye primordium of vertebrates initially forms exactly at the side of the head. Later, the eyeball architecture is tuned to see ahead with better visual acuity, but its molecular basis is unknown. The position of both eyes in the face alters in patients with holoprosencephaly due to Sonic hedgehog (Shh) mutations that disturb the development of the ventral midline of the neural tube. However, patient phenotypes vary extensively, and microforms without a brain anomaly relate instead to alternation of gene expression of the Shh signaling center in the facial primordia. We identified novel missense mutations of the Shh gene in two patients with a dislocated fovea, where the photoreceptor cells are condensed. Functional assays showed that Shh upregulates Patched and Gli and downregulates Pax6, and that Shh mutations alter these activities. Gain of function of Shh in a chick embryo retards retinal development and eyeball growth depending on the location of Shh expression, while loss of function of Shh promotes these features. We postulate that a signaling molecule like Shh that emanates from the face controls the extent of differentiation of the neural retina in a position-specific manner and that this may result in the formation of the fovea at the correct location. Full article

Weed detection is a crucial step in achieving intelligent weeding for vegetables. Currently, research on vegetable weed detection technology is relatively limited, and existing detection methods still face challenges due to complex natural conditions, resulting in low detection accuracy and efficiency. This paper proposes the YOLOv8-EGC-Fusion (YEF) model, an enhancement based on the YOLOv8 model, to address these challenges. This model introduces plug-and-play modules: (1) The Efficient Group Convolution (EGC) module leverages convolution kernels of various sizes combined with group convolution techniques to significantly reduce computational cost. Integrating this EGC module with the C2f module creates the C2f-EGC module, strengthening the model’s capacity to grasp local contextual information. (2) The Group Context Anchor Attention (GCAA) module strengthens the model’s capacity to capture long-range contextual information, contributing to improved feature comprehension. (3) The GCAA-Fusion module effectively merges multi-scale features, addressing shallow feature loss and preserving critical information. Leveraging GCAA-Fusion and PAFPN, we developed an Adaptive Feature Fusion (AFF) feature pyramid structure that amplifies the model’s feature extraction capabilities. To ensure effective evaluation, we collected a diverse dataset of weed images from various vegetable fields. A series of comparative experiments was conducted to verify the detection effectiveness of the YEF model. The results show that the YEF model outperforms the original YOLOv8 model, Faster R-CNN, RetinaNet, TOOD, RTMDet, and YOLOv5 in detection performance. The detection metrics achieved by the YEF model are as follows: precision of 0.904, recall of 0.88, F1 score of 0.891, and mAP0.5 of 0.929. In conclusion, the YEF model demonstrates high detection accuracy for vegetable and weed identification, meeting the requirements for precise detection. Full article

In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder their practical application. To address these issues, this study proposes an improved YOLOv7-tiny model designed to deliver efficient, accurate, and lightweight pest detection solutions. The main improvements are as follows: 1. Lightweight Network Design: The backbone network is optimized by integrating GhostNet and Dynamic Region-Aware Convolution (DRConv) to enhance computational efficiency. 2. Feature Sharing Enhancement: The introduction of a Cross-layer Feature Sharing Network (CotNet Transformer) strengthens feature fusion and extraction capabilities. 3. Activation Function Optimization: The traditional ReLU activation function is replaced with the Gaussian Error Linear Unit (GELU) to improve nonlinear expression and classification performance. Experimental results demonstrate that the improved model surpasses YOLOv7-tiny in accuracy, inference speed, and model size, achieving a MAP@0.5 of 92.8%, reducing inference time to 4.0 milliseconds, and minimizing model size to just 4.8 MB. Additionally, compared to algorithms like Faster R-CNN, SSD, and RetinaNet, the improved model delivers superior detection performance. In conclusion, the improved YOLOv7-tiny provides an efficient and practical solution for intelligent pest detection in agriculture and forestry. Full article

Tourists’ near-field passive perception and identification in mountain areas faces challenges related to long distances, small targets, varied-pose scenarios, facial occlusion, etc. To address this issue, this paper proposes an innovative technical framework based on a face-to-body (F2B) two-step iterative method aimed at enhancing the passive perception and tracking of tourists in complex mountain environments by integrating and coordinating body features with facial features. The F2B technical framework comprises three main components: target feature acquisition, multi-feature coupled re-identification, and target positioning and tracking. Initially, the faces and bodies of tourists are extracted from real-time video streams using the RetinaFace and YOLOX models, respectively. The ArcFace model is then employed to extract the facial features of the target tourists, linking them with the faces detected by RetinaFace. Subsequently, a multi-feature database is constructed using the Hungarian algorithm to facilitate the automatic matching of the face and body of the same tourist. Finally, the Fast-ReID model and a spatial position algorithm are utilized for the re-identification of tourist targets and tracking their dynamic paths. Based on public and actual scene datasets, deployment and testing in the Yimeng Mountain Scenic Area have demonstrated that the accuracy index AP of the F2B model reaches 88.03%, with a recall of 90.28%, achieving an overall identification accuracy of approximately 90% and a false alarm rate of less than 5%. This result significantly improves the accuracy of SOTA facial recognition models in the complex environments of mountainous scenic spots. It effectively addresses the challenges associated with the low identification accuracy of non-cooperative targets in these areas through a ground video sensing network. Furthermore, it offers technical support for spatiotemporal information regarding near-field passive perception and path tracking of tourists in mountain scenic spots and showcasing broad application prospects. Full article

Inherited retinal diseases (IRDs) represent a diverse group of genetic disorders leading to progressive degeneration of the retina due to mutations in over 280 genes. This review focuses on the various methodologies for the preclinical characterization and evaluation of adeno-associated virus (AAV)-mediated gene therapy as a potential treatment option for IRDs, particularly focusing on gene therapies targeting mutations, such as those in the RPE65 and FAM161A genes. AAV vectors, such as AAV2 and AAV5, have been utilized to deliver therapeutic genes, showing promise in preserving vision and enhancing photoreceptor function in animal models. Despite their advantages—including high production efficiency, low pathogenicity, and minimal immunogenicity—AAV-mediated therapies face limitations such as immune responses beyond the retina, vector size constraints, and challenges in large-scale manufacturing. This review systematically compares different experimental models used to investigate AAV-mediated therapies, such as mouse models, human retinal explants (HREs), and induced pluripotent stem cell (iPSC)-derived retinal organoids. Mouse models are advantageous for genetic manipulation and detailed investigations of disease mechanisms; however, anatomical differences between mice and humans may limit the translational applicability of results. HREs offer valuable insights into human retinal pathophysiology but face challenges such as tissue degradation and lack of systemic physiological effects. Retinal organoids, on the other hand, provide a robust platform that closely mimics human retinal development, thereby enabling more comprehensive studies on disease mechanisms and therapeutic strategies, including AAV-based interventions. Specific outcomes targeted in these studies include vision preservation and functional improvements of retinas damaged by genetic mutations. This review highlights the strengths and weaknesses of each experimental model and advocates for their combined use in developing targeted gene therapies for IRDs. As research advances, optimizing AAV vector design and delivery methods will be critical for enhancing therapeutic efficacy and improving clinical outcomes for patients with IRDs. Full article

Background/Objectives: Age-related macular degeneration (AMD) and retinitis pigmentosa (RP) are leading causes of vision loss, with AMD affecting older populations and RP being a rarer, genetically inherited condition. Both diseases result in progressive retinal degeneration, for which current treatments remain inadequate in advanced stages. This review aims to provide an overview of the retina’s anatomy and physiology, elucidate the pathophysiology of AMD and RP, and evaluate emerging cell-based therapies for these conditions. Methods: A comprehensive review of the literature was conducted, focusing on cell therapy approaches, including embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), mesenchymal stem cells (MSCs), and retinal progenitor cells. Preclinical and clinical studies were analyzed to assess therapeutic potential, with attention to mechanisms such as cell replacement, neuroprotection, and paracrine effects. Relevant challenges, including ethical concerns and clinical translation, were also explored. Results: Cell-based therapies demonstrate potential for restoring retinal function and slowing disease progression through mechanisms like neuroprotection and cell replacement. Preclinical trials show promising outcomes, but clinical studies face significant hurdles, including challenges in cell delivery and long-term efficacy. Combination therapies integrating gene editing and biomaterials offer potential future advancements. Conclusions: While cell-based therapies for AMD and RP have made significant progress, substantial barriers to clinical application remain. Further research is essential to overcome these obstacles, improve delivery methods, and ensure the safe and effective translation of these therapies into clinical practice. Full article

Purpose: The clinical use of en face optical coherence tomography (OCT) has revealed nerve fiber layer clefts in the retinal nerve fibers in eyes with macula-centered epiretinal membranes (ERMs). The purpose of this study is to describe the location and the extent of retinal nerve fiber layer (RNFL) clefts in eyes with symptomatic ERMs. Methods: We conducted a retrospective review of 17 individual eyes in 17 patients with symptomatic ERMs and a control group of 10 healthy eyes from 10 subjects who had been examined for unrelated causes. The examinations performed included best-corrected visual acuity, rebound tonometry, fundus photography, structural OCT and angiographic OCT (OCTA) made in the form of 12 × 12 mm angiographic volume scans. Results: Hyporeflective RNFL clefts, seen in 14 out of 17 eyes with ERMs, were sharply demarcated in the en face presentation of slabs extending from the internal limiting membrane through the RNFL or including only the latter. The clefts were capillary-free on OCTA scans and formed depressions of the retinal surface. Most of the clefts were adjacent to and followed the course of the retinal trunk vessels, but clefts were also seen along smaller macular vessels and beyond the retinal vascular arcades. Conclusions: Paravascular RNFL clefts can be observed beyond the vascular arcades and adjacent to small vessels on OCTA block scan data. This suggests that the direction and magnitude of tractional displacement of the inner retina in eyes with epimacular membranes can extend beyond the vascular arcades and add to an improved analysis of abnormal fundus findings. Full article

In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest target detection algorithm, GLU-YOLOv8, designed for complex scenes based on an enhanced version of You Only Look Once version 8 (YOLOv8). The algorithm introduces the SCYLLA-IOU (SIOU) loss function, which enhances the model generalization to various pest sizes and shapes by ensuring smoothness and reducing oscillations during training. Additionally, the algorithm incorporates the Convolutional Block Attention Module (CBAM) and Locality Sensitive Kernel (LSK) attention mechanisms to boost the pest target features. A novel Gated Linear Unit CONV (GLU-CONV) is also introduced to enhance the model’s perceptual and generalization capabilities while maintaining performance. Furthermore, GLU-YOLOv8 includes a small-object detection layer with a feature map size of 160 × 160 to extract more features of small-target pests, thereby improving detection accuracy and enabling more precise localization and identification of small-target pests. The study conducted a comparative analysis between the GLU-YOLOv8 model and other models, such as YOLOv8, Faster RCNN, and RetinaNet, to evaluate detection accuracy and precision. In the Scolytidae forestry pest dataset, GLU-YOLOv8 demonstrated an improvement of 8.2% in mAP@0.50 for small-target detection compared to the YOLOv8 model, with a resulting mAP@0.50 score of 97.4%. Specifically, on the IP102 dataset, GLU-YOLOv8 outperforms the YOLOv8 model with a 7.1% increase in mAP@0.50 and a 5% increase in mAP@0.50:0.95, reaching 58.7% for mAP@0.50. These findings highlight the significant enhancement in the accuracy and recognition rate of small-target detection achieved by GLU-YOLOv8, along with its efficient operational performance. This research provides valuable insights for optimizing small-target detection models for various pests and diseases. Full article