Diagnosis Of Ophthalmic Diseases Research Articles

Bibliometric analysis of ophthalmic OCT and OCT angiography research trends over the past 20 years.

Optical coherence tomography (OCT) and OCT angiography (OCTA) are widely used in the diagnosis of ophthalmic diseases. This study aims to provide a comprehensive bibliometric analysis of ophthalmologic OCT and OCT angiography. We retrieved publications on ophthalmic OCT and OCTA from 2003 to 2022 from the Web of Science Core Collection and used bibliometric tools to analyze and visualize the distribution, trend, and hotspots. In total, 20,817 articles written by 48,160 authors from 106 countries were selected. The number of publications has significantly increased. In the last two decades, the USA was the most productive country and received the highest citations. The most productive journal was Investigative Ophthalmology and Visual Science and received the highest number of citations. Moorfields Eye Hospital was the most productive institution. Bandello F published the most papers, while Spaide RF was the highest cited author. SPAIDE RF, 2011, AM J Ophthalmology was the most cited document. "OCT", "glaucoma" and "OCTA" were three hotspots in the last two decades. "Vessel density" and "deep learning" would be research hotspots in the future. The bibliometric analysis of ophthalmic OCT and OCTA research over the past two decades on keywords, authors, citations, hotspots and trends will provide global researchers with valuable information for future research and cooperation.

Enhancing the ophthalmic AI assessment with a fundus image quality classifier using local and global attention mechanisms.

The assessment of image quality (IQA) plays a pivotal role in the realm of image-based computer-aided diagnosis techniques, with fundus imaging standing as the primary method for the screening and diagnosis of ophthalmic diseases. Conventional studies on fundus IQA tend to rely on simplistic datasets for evaluation, predominantly focusing on either local or global information, rather than a synthesis of both. Moreover, the interpretability of these studies often lacks compelling evidence. In order to address these issues, this study introduces the Local and Global Attention Aggregated Deep Neural Network (LGAANet), an innovative approach that integrates both local and global information for enhanced analysis. The LGAANet was developed and validated using a Multi-Source Heterogeneous Fundus (MSHF) database, encompassing a diverse collection of images. This dataset includes 802 color fundus photography (CFP) images (302 from portable cameras), and 500 ultrawide-field (UWF) images from 904 patients with diabetic retinopathy (DR) and glaucoma, as well as healthy individuals. The assessment of image quality was meticulously carried out by a trio of ophthalmologists, leveraging the human visual system as a benchmark. Furthermore, the model employs attention mechanisms and saliency maps to bolster its interpretability. In testing with the CFP dataset, LGAANet demonstrated remarkable accuracy in three critical dimensions of image quality (illumination, clarity and contrast based on the characteristics of human visual system, and indicates the potential aspects to improve the image quality), recording scores of 0.947, 0.924, and 0.947, respectively. Similarly, when applied to the UWF dataset, the model achieved accuracies of 0.889, 0.913, and 0.923, respectively. These results underscore the efficacy of LGAANet in distinguishing between varying degrees of image quality with high precision. To our knowledge, LGAANet represents the inaugural algorithm trained on an MSHF dataset specifically for fundus IQA, marking a significant milestone in the advancement of computer-aided diagnosis in ophthalmology. This research significantly contributes to the field, offering a novel methodology for the assessment and interpretation of fundus images in the detection and diagnosis of ocular diseases.

An automatic glaucoma grading method based on attention mechanism and EfficientNet-B3 network.

Glaucoma infection is rapidly spreading globally and the number of glaucoma patients is expected to exceed 110 million by 2040. Early identification and detection of glaucoma is particularly important as it can easily lead to irreversible vision damage or even blindness if not treated with intervention in the early stages. Deep learning has attracted much attention in the field of computer vision and has been widely studied especially in the recognition and diagnosis of ophthalmic diseases. It is challenging to efficiently extract effective features for accurate grading of glaucoma in a limited dataset. Currently, in glaucoma recognition algorithms, 2D fundus images are mainly used to automatically identify the disease or not, but do not distinguish between early or late stages; however, in clinical practice, the treatment of early and late glaucoma is not the same, so it is more important to proceed to achieve accurate grading of glaucoma. This study uses a private dataset containing modal data, 2D fundus images, and 3D-OCT scanner images, to extract the effective features therein to achieve an accurate triple classification (normal, early, and moderately advanced) for optimal performance on various measures. In view of this, this paper proposes an automatic glaucoma classification method based on the attention mechanism and EfficientNetB3 network. The EfficientNetB3 network and ResNet34 network are built to extract and fuse 2D fundus images and 3D-OCT scanner images, respectively, to achieve accurate classification. The proposed auto-classification method minimizes feature redundancy while improving classification accuracy, and incorporates an attention mechanism in the two-branch model, which enables the convolutional neural network to focus its attention on the main features of the eye and discard the meaningless black background region in the image to improve the performance of the model. The auto-classification method combined with the cross-entropy function achieves the highest accuracy up to 97.83%. Since the proposed automatic grading method is effective and ensures reliable decision-making for glaucoma screening, it can be used as a second opinion tool by doctors, which can greatly reduce missed diagnosis and misdiagnosis by doctors, and buy more time for patient's treatment.

Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation.

Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce. In addition, existing SLO image segmentation methods still have difficulty in balancing accuracy and model parameters. This paper proposes a SLO image segmentation model based on lightweight U-Net architecture called MBRNet, which solves the problems in the current research through Multi-scale Bottleneck Residual (MBR) module and attention mechanism. Concretely speaking, the MBR module expands the receptive field of the model at a relatively low computational cost and retains more detailed information. Attention Gate (AG) module alleviates the disturbance of noise so that the network can concentrate on vascular characteristics. Experimental results on two public SLO datasets demonstrate that by comparison to existing methods, the MBRNet has better segmentation performance with relatively few parameters.

Transformer-based cross-modal multi-contrast network for ophthalmic diseases diagnosis

Automatic diagnosis of various ophthalmic diseases from ocular medical images is vital to support clinical decisions. Most current methods employ a single imaging modality, especially 2D fundus images. Considering that the diagnosis of ophthalmic diseases can greatly benefit from multiple imaging modalities, this paper further improves the accuracy of diagnosis by effectively utilizing cross-modal data. In this paper, we propose Transformer-based cross-modal multi-contrast network for efficiently fusing color fundus photograph (CFP) and optical coherence tomography (OCT) modality to diagnose ophthalmic diseases. We design multi-contrast learning strategy to extract discriminate features from cross-modal data for diagnosis. Then channel fusion head captures the semantically shared information across different modalities and the similarity features between patients of the same category. Meanwhile, we use a class-balanced training strategy to cope with the situation that medical datasets are usually class-imbalanced. Our method is evaluated on public benchmark datasets for cross-modal ophthalmic disease diagnosis. The experimental results demonstrate that our method outperforms other approaches. The codes and models are available at https://github.com/ecustyy/tcmn.

TCU-Net: Transformer Embedded in Convolutional U-Shaped Network for Retinal Vessel Segmentation.

Optical coherence tomography angiography (OCTA) provides a detailed visualization of the vascular system to aid in the detection and diagnosis of ophthalmic disease. However, accurately extracting microvascular details from OCTA images remains a challenging task due to the limitations of pure convolutional networks. We propose a novel end-to-end transformer-based network architecture called TCU-Net for OCTA retinal vessel segmentation tasks. To address the loss of vascular features of convolutional operations, an efficient cross-fusion transformer module is introduced to replace the original skip connection of U-Net. The transformer module interacts with the encoder's multiscale vascular features to enrich vascular information and achieve linear computational complexity. Additionally, we design an efficient channel-wise cross attention module to fuse the multiscale features and fine-grained details from the decoding stages, resolving the semantic bias between them and enhancing effective vascular information. This model has been evaluated on the dedicated Retinal OCTA Segmentation (ROSE) dataset. The accuracy values of TCU-Net tested on the ROSE-1 dataset with SVC, DVC, and SVC+DVC are 0.9230, 0.9912, and 0.9042, respectively, and the corresponding AUC values are 0.9512, 0.9823, and 0.9170. For the ROSE-2 dataset, the accuracy and AUC are 0.9454 and 0.8623, respectively. The experiments demonstrate that TCU-Net outperforms state-of-the-art approaches regarding vessel segmentation performance and robustness.

An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship

The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided diagnosis systems (CADs) have demonstrated their potential to reduce reading time and discrepancy amongst readers. However, the obscure reasoning of deep neural networks (DNNs) has been the leading cause to reluctance in its clinical use as CAD systems. Here, we present a novel architectural and algorithmic design of DNNs to comprehensively identify 15 abnormal retinal findings and diagnose 8 major ophthalmic diseases from macula-centered fundus images with the accuracy comparable to experts. We then define a notion of counterfactual attribution ratio (CAR) which luminates the system’s diagnostic reasoning, representing how each abnormal finding contributed to its diagnostic prediction. By using CAR, we show that both quantitative and qualitative interpretation and interactive adjustment of the CAD result can be achieved. A comparison of the model’s CAR with experts’ finding-disease diagnosis correlation confirms that the proposed model identifies the relationship between findings and diseases similarly as ophthalmologists do.

MINet: Multi-scale input network for fundus microvascular segmentation

Vessel segmentation in fundus images is a key procedure in the diagnosis of ophthalmic diseases, which can play a role in assisting doctors in diagnosis. Although current deep learning-based methods can achieve high accuracy in segmenting fundus vessel images, the results are not satisfactory in segmenting microscopic vessels that are close to the background region. The reason for this problem is that thin blood vessels contain very little information, with the convolution operation of each layer in the deep network, this part of the information will be randomly lost. To improve the segmentation ability of the small blood vessel region, a multi-input network (MINet) was proposed to segment vascular regions more accurately. We designed a multi-input fusion module (MIF) in the encoder, which is proposed to acquire multi-scale features in the encoder stage while preserving the microvessel feature information. In addition, to further aggregate multi-scale information from adjacent regions, a multi-scale atrous spatial pyramid (MASP) module is proposed. This module can enhance the extraction of vascular information without reducing the resolution loss. In order to better recover segmentation results with details, we designed a refinement module, which acts on the last layer of the network output to refine the results of the last layer of the network to get more accurate segmentation results. We use the HRF, CHASE_DB1 public dataset to validate the fundus vessel segmentation performance of the MINet model. Also, we merged these two public datasets with our collected Ultra-widefield fundus image (UWF) data as one dataset to test the generalization ability of the model. Experimental results show that MINet achieves an F1 score of 0.8324 on the microvessel segmentation task, achieving a high accuracy compared to the current mainstream models.

Retinal blood vessel segmentation by using the MS-LSDNet network and geometric skeleton reconnection method

Automatic retinal blood vessel segmentation is a key link in the diagnosis of ophthalmic diseases. Recent deep learning methods have achieved high accuracy in vessel segmentation but still face challenges in maintaining vascular structural connectivity. Therefore, this paper proposes a novel retinal blood vessel segmentation strategy that includes three stages: vessel structure detection, vessel branch extraction and broken vessel segment reconnection. First, we propose a multiscale linear structure detection network (MS-LSDNet), which improves the detection ability of fine blood vessels by learning the types of rich hierarchical features. In addition, to maintain the connectivity of the vascular structure in the process of binarization of the vascular probability map, an adaptive hysteresis threshold method for vascular extraction is proposed. Finally, we propose a vascular tree structure reconstruction algorithm based on a geometric skeleton to connect the broken vessel segments. Experimental results on three publicly available datasets show that compared with current state-of-the-art algorithms, our strategy effectively maintains the connectivity of retinal vascular tree structure.

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation.

Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datasets do not have enough images and are unevenly distributed. The particle swarm optimization algorithm has been proved to be a good tool to deal with various extreme value problems, which requires little data and does not require pre-training. In this paper, the segmentation problem is converted to a set of extreme value problems. The scheme performs data preprocessing based on the features of the fundus map, reduces noise on the picture, and simplifies the search space for particles. The search space is divided into multiple sub-search spaces according to the number of subgroups, and the particles inside the subgroups search for the optimal solution in their respective sub-search spaces. The gradient values are used to calculate the fitness of particles and contours. The entire group is divided into some subgroups. Every particle flies in their exploration for the best solution. During the iteration, particles are not only influenced by local and global optimal solutions but also additionally attracted by particles between adjacent subgroups. By collaboration and information sharing, the particles are capable of obtaining accurate disc segmentation. This method has been tested with the Drishti-GS and RIM-ONE V3 dataset. Compared to several state-of-the-art methods, the proposed method substantially improves the optic disc segmentation results on the tested datasets, which demonstrates the superiority of the proposed work.

Application of nanotechnology in ophthalmology: Where are we?

Nanotechnology is a subject that studies, processes, and applies various functional materials, equipment, and systems, and controls substances on a nanoscale. Nanomedicine refers to its application in diagnosing, treating, preventing, and monitoring various diseases. Drugs administered through eye drops must travel a long distance to avoid various eye barriers reaching the posterior segment of the eye, to achieve the lowest drug level. This review focuses on nanotechnology-based eye disease treatment systems and highlights the obstacles affecting the drug management of eyes and nano-systems for the treatment of eye diseases. This paper summarizes the development prospect of nanotechnology and the challenges it faces in the treatment and diagnosis of ophthalmic diseases, to provide information and new ideas for the implementation of treatment and the development of a refractory eye disease management system.

Application of metagenomics in the diagnosis of ophthalmic diseases

Application of metagenomics in the diagnosis of ophthalmic diseases

Diagnosis of Ophthalmic Diseases in Fundus Image by Using Deep Learning

Abstract: In the human eye, Damage in the retina may cause ophthalmic diseases like cataracts, AMD, Hypertensive retinopathy, myopia, etc. To cure these diseases, many ophthalmologists use retinal fundus images as an important information source to find out ophthalmic diseases. Multiple techniques have been introduced for the screening of ocular diseases. Today’s world is in great demand to find out ocular diseases by using deep learning and machine learning techniques. This paper uses pre-trained deep neural networks to determine five categories of ophthalmic diseases such as cataract, AMD, Hypertensive retinopathy, myopia, and normal. Dataset is created into binary and multiclass, then trained on Resnet-101 of convolutional neural network (CNN) and evaluated. The accuracy of this model is found to be 90.38% and 88.5% for binary and multiclass respectively. Keywords: Retinal fundus image, Ocular diseases, CNN, ResNet, deep learning. Image processing, Ensemble classifier

CSU-Net: A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images.

Blood vessel segmentation in fundus images is a critical procedure in the diagnosis of ophthalmic diseases. Recent deep learning methods achieve high accuracy in vessel segmentation but still face the challenge to segment the microvascular and detect the vessel boundary. This is due to the fact that common Convolutional Neural Networks (CNN) are unable to preserve rich spatial information and a large receptive field simultaneously. Besides, CNN models for vessel segmentation usually are trained by equal pixel level cross-entropy loss, which tend to miss fine vessel structures. In this paper, we propose a novel Context Spatial U-Net (CSU-Net) for blood vessel segmentation. Compared with the other U-Net based models, we design a two-channel encoder: a context channel with multi-scale convolution to capture more receptive field and a spatial channel with large kernel to retain spatial information. Also, to combine and strengthen the features extracted from two paths, we introduce a feature fusion module (FFM) and an attention skip module (ASM). Furthermore, we propose a structure loss, which adds a spatial weight to cross-entropy loss and guide the network to focus more on the thin vessels and boundaries. We evaluated this model on three public datasets: DRIVE, CHASE-DB1 and STARE. The results show that the CSU-Net achieves higher segmentation accuracy than the current state-of-the-art methods.

Optical coherent angiography in the diagnosis of ophthalmic diseases. Problems, prospects. Review

Optical coherent angiography in the diagnosis of ophthalmic diseases. Problems, prospects. Review

Intravitreous Cutaneous Metastatic Melanoma in the Era of Checkpoint Inhibition: Unmasking and Masquerading

Cutaneous melanoma metastatic to the vitreous is very rare. This study investigated the clinical findings, treatment, and outcome of patients with metastatic cutaneous melanoma to the vitreous. Most patients received checkpoint inhibition for the treatment of systemic disease, and the significance of this was explored. Multicenter, retrospective cohort study. Fourteen eyes of 11 patients with metastatic cutaneous melanoma to the vitreous. Clinical records, including fundus photography and ultrasound results, were reviewed retrospectively, and relevant data were recorded for each patient eye. Clinical features at presentation, ophthalmic and systemic treatments, and outcomes. The median age at presentation of ophthalmic disease was 66 years (range, 23-88 years), and the median follow-up from diagnosis of ophthalmic disease was 23 months. Ten of 11 patients were treated with immune checkpoint inhibition at some point in the treatment course. The median time from starting immunotherapy to ocular symptoms was 17 months (range, 4.5-38 months). Half of eyes demonstrated amelanotic vitreous debris. Five eyes demonstrated elevated intraocular pressure, and 4 eyes demonstrated a retinal detachment. Six patients showed metastatic disease in the central nervous system. Ophthalmic treatment included external beam radiation (30-40 Gy) in 6 eyes, intravitreous melphalan (10-20 μg) in 4 eyes, enucleation of 1 eye, and local observation while receiving systemic treatment in 2 eyes. Three eyes received intravitreous bevacizumab for neovascularization. The final Snellen visual acuity ranged from 20/20 to no light perception. The differential diagnosis of vitreous debris in the context of metastatic cutaneous melanoma includes intravitreal metastasis, and this seems to be particularly apparent during this era of treatment with checkpoint inhibition. External beam radiation, intravitreous melphalan, and systemic checkpoint inhibition can be used in the treatment of ophthalmic disease. Neovascular glaucoma and retinal detachments may occur, and most eyes show poor visual potential. Approximately one quarter of patients demonstrated ocular disease that preceded central nervous system metastasis. Patients with visual symptoms or vitreous debris in the context of metastatic cutaneous melanoma would benefit from evaluation by an ophthalmic oncologist.

Diagnosis, treatment, and outcome of and risk factors for ophthalmic disease in leopard geckos (Eublepharis macularius) at a veterinary teaching hospital: 52 cases (1985-2013).

OBJECTIVE To describe diagnosis, treatment, and outcome of and risk factors for ophthalmic disease in leopard geckos (Eublepharis macularius) evaluated at a veterinary teaching hospital. DESIGN Retrospective case series. ANIMALS 112 of 144 (78%) leopard geckos that were evaluated at a veterinary teaching hospital in January 1985 through October 2013 and for which sufficient medical record information was available. PROCEDURES Information from medical records was used to identify leopard geckos with ophthalmic disease, characterize cases, and determine risk factors for the presence of ophthalmic disease. RESULTS Of the 112 leopard geckos, 52 (46%) had ophthalmic disease (mainly corneal or conjunctival disease). Female geckos were less likely to have ophthalmic disease, and there was a positive association between increasing age and ophthalmic disease. Use of a paper towel substrate, absence of any heat source, and lack of vitamin A supplementation were positively associated with a diagnosis of ophthalmic disease. Head dysecdysis was the only concurrent disorder significantly associated with ophthalmic disease. At necropsy, 5 affected leopard geckos had squamous metaplasia of the conjunctivae. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that ophthalmic disease is a common finding in leopard geckos. The cause of ocular surface disease in leopard geckos may be multifactorial, and hypovitaminosis A may be an important risk factor. Although animals receiving supplemental vitamin A were less likely to have ophthalmic disease, further understanding is required regarding the metabolism of and nutritional requirements for vitamin A in leopard geckos.

Compilation of Yan ke zheng zong yuan ji qi wei (Orthodox Ophthalmology: Revealing the Mystery of Eye Disease) and its academic achievements

As an ophthalmic monograph compiled to systemize and elaborate the Yuan ji qi wei (Revealing the Mystery of Eye Disease) written to explore the gist of the original author, a valuable edition of the original Yuan ji qi wei. SHI Shi-de made reviews and comments to each eye disease included in the first volume of Yuan ji qi wei, followed by the rhymes on the etiology, manifestations, treatment and prescriptions written by his son SHI Guang, and rearranged and systemized the whole text made by his little brother SHI Shi-qi for better memory and popularization. He stressed the significant position of Yuan ji qi wei. The achievements of the Yan ke zheng zong yuan ji qi wei can be summarized into 4 points: the "18 disease" of eye disease is the guidelines for the differential diagnosis of ophthalmic diseases; Shi's annotation developed the essence of Ni's theory; SHI Guang's rhymes facilitated the spread of Ni's theory; the "hints for prescription" being the standards for the treatment of eye disease.

Biallelic Mutations in PLA2G5, Encoding Group V Phospholipase A2, Cause Benign Fleck Retina

Flecked-retina syndromes, including fundus flavimaculatus, fundus albipunctatus, and benign fleck retina, comprise a group of disorders with widespread or limited distribution of yellow-white retinal lesions of various sizes and configurations. Three siblings who have benign fleck retina and were born to consanguineous parents are the basis of this report. A combination of homozygosity mapping and exome sequencing helped to identify a homozygous missense mutation, c.133G>T (p.Gly45Cys), in PLA2G5, a gene encoding a secreted phospholipase (group V phospholipase A(2)). A screen of a further four unrelated individuals with benign fleck retina detected biallelic variants in the same gene in three patients. In contrast, no loss of function or common (minor-allele frequency>0.05%) nonsynonymous PLA2G5 variants have been previously reported (EVS, dbSNP, 1000 Genomes Project) or were detected in an internal database of 224 exomes (from subjects with adult onset neurodegenerative disease and without a diagnosis of ophthalmic disease). All seven affected individuals had fundoscopic features compatible with those previously described in benign fleck retina and no visual or electrophysiological deficits. No medical history of major illness was reported. Levels of low-density lipoprotein were mildly elevated in two patients. Optical coherence tomography and fundus autofluorescence findings suggest that group V phospholipase A(2) plays a role in the phagocytosis of photoreceptor outer-segment discs by the retinal pigment epithelium. Surprisingly, immunohistochemical staining of human retinal tissue revealed localization of the protein predominantly in the inner and outer plexiform layers.

Effect of highly active antiretroviral therapy (HAART) on the natural history of ocular manifestations in HIV-infected children

To determine the effect of highly active antiretroviral therapy (HAART) on the natural history of ocular manifestations in HIV-infected children. All of the HIV-infected children attending the Institute of Pediatrics, University of Milan, Milan, Italy, between 1982 and 2004 were studied. Every 3 months, they were physically examined and underwent indirect fundoscopy conducted by an experienced ophthalmologist; if diagnosed as having eye disease, they were evaluated by the ophthalmologist every week while on specific therapy and monthly thereafter. The clinical and laboratory findings before and after the introduction of HAART were compared. The cohort consisted of 117 HIV-infected children (61 males), with a follow-up ranging from 0.09 to 22.31 years (median, 16.33 years). A total of nine cases of ocular involvement (7.7%) were diagnosed between 1983 and 1994, before the introduction of HAART. All nine children died 4-24 months (median, 15 months) after the diagnosis of ophthalmic disease. No case of ocular involvement was observed after the introduction of HAART (P=0.011 vs. before HAART). The introduction of HAART has had a significant impact on the natural history of ocular manifestations in HIV-infected children, thus suggesting that a reduction in the frequency of ophthalmologic follow-up should be considered for HAART-treated HIV-infected children with immune reconstitution and no visual symptom.