Hasil untuk "Ophthalmology"

Bacharach J, Sadri E, Sawhney G et al.

Jason Bacharach,1 Ehsan Sadri,2 Gagan Sawhney,3 Casey Kopczynski,4 Mohinder M Merchea4 1North Bay Eye Associates, Inc, Petaluma, CA, USA; 2Visionary Eye Institute, Newport Beach, CA, USA; 3Georgia Eye Partners, Atlanta, GA, USA; 4Alcon, Inc, Fort Worth, TX, USACorrespondence: Jason Bacharach, North Bay Eye Associates, Inc, 104 Lynch Creek Way, Suite 15, Petaluma, CA, USA, Tel +1(707) 762-3573, Fax +1 (707) 762-6873, Email jbacharach@northbayeye.comPurpose: To determine the effect on intraocular pressure (IOP) of switching to a once-daily netarsudil/latanoprost fixed dose combination (FDC) from various topical treatment regimens including latanoprost monotherapy or latanoprost combined with other IOP-lowering agents for the treatment of open-angle glaucoma or ocular hypertension.Methods: A total of 136 participants enrolled. Eligible participants were aged ≥ 18 years and had a current diagnosis of open-angle glaucoma or ocular hypertension. Additional inclusion criteria were current treatment regimens with latanoprost monotherapy, latanoprost plus 1 additional IOP-lowering agent, or latanoprost plus 2 agents; current IOP-lowering regimen stable for ≥ 30 days prior to baseline visit; treated morning IOP ≥ 20 mmHg at baseline visit; and best corrected visual acuity (BCVA) of 20/100 or better in both eyes. Regardless of their initial regimens, all participants stopped their IOP-lowering medication(s) and were switched directly to netarsudil/latanoprost FDC alone.Results: Participants experienced substantial reductions in IOP. At week 12, the mean percent change from baseline in IOP was − 18.5% (SD 18.96) in the overall study population and was similar in the latanoprost monotherapy group (− 21.2% [SD 17.46]), the latanoprost +1 agent group (− 15.7% [SD 21.91]), and the latanoprost +2 agents group (− 16.9% [SD 17.31]). Less than one-third of participants (31.6%) experienced any ocular adverse event or an ocular adverse event related to treatment (27.2%). The most common ocular adverse event was conjunctival hyperemia (18.4%). Most ocular adverse events were mild, and two severe ocular adverse events of hyperemia (1.5%) were reported; no serious ocular adverse events were reported.Conclusion: In this study, additional IOP lowering was achievable when patients switched to netarsudil/latanoprost FDC after treatment with latanoprost alone or latanoprost with 1 or 2 additional agents. The once-daily administration of netarsudil/latanoprost FDC and reduced treatment burden for those on latanoprost combined with additional agents may prove more manageable for patients.Keywords: glaucoma, intraocular pressure, prostaglandin analog, rho-kinase inhibitor

Fei Yuhuan, Sun Xufei, Zang Ran et al.

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW module are designed to improve the backbone network, enhancing the model's ability to perceive micro-lesions. Secondly, a fea-ture-focused diffusion pyramid network FDPN is designed to fully integrate multi-scale context information, further improving the model's ability to perceive micro-lesions. Finally, a lightweight shared detection head GSDHead is designed to reduce the model's parameter count, making it more deployable on re-source-constrained devices. Experimental results show that compared with the base model YOLOv8n, the improved model reduces the parameter count by 20.7%, increases mAP@0.5 by 4.1%, and improves the recall rate by 7.9%. Compared with single-stage mainstream algorithms such as YOLOv5n and YOLOv10n, YOLO-KFG demonstrates significant advantages in both detection accuracy and efficiency.

Akshat Dubey, Aleksandar Anžel, Bahar İlgen et al.

Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is driven by increasingly sophisticated model architectures, such as ResNets, Vision Transformers, and Hybrid Convolutional Neural Networks, that offer enhanced performance at the cost of greater complexity. This complexity often compromises model explainability and interpretability. SHAP has emerged as a prominent method for providing interpretable visualizations that aid domain experts in understanding model predictions. However, SHAP explanations can be unstable and unreliable in the presence of epistemic and aleatoric uncertainty. In this study, we address this challenge by using Dirichlet posterior sampling and Dempster-Shafer theory to quantify the uncertainty that arises from these unstable explanations in medical imaging applications. The framework uses a belief, plausible, and fusion map approach alongside statistical quantitative analysis to produce quantification of uncertainty in SHAP. Furthermore, we evaluated our framework on three medical imaging datasets with varying class distributions, image qualities, and modality types which introduces noise due to varying image resolutions and modality-specific aspect covering the examples from pathology, ophthalmology, and radiology, introducing significant epistemic uncertainty.

Chengzhi Liu, Zile Huang, Zhe Chen et al.

Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data privacy concerns, missing data modalities are common in real-world scenarios. Existing deep learning methods tend to address it by learning an implicit latent subspace representation for different modality combinations. We identify two significant limitations of these methods: (1) implicit representation constraints that hinder the model's ability to capture modality-specific information and (2) modality heterogeneity, causing distribution gaps and redundancy in feature representations. To address these, we propose an Incomplete Modality Disentangled Representation (IMDR) strategy, which disentangles features into explicit independent modal-common and modal-specific features by guidance of mutual information, distilling informative knowledge and enabling it to reconstruct valuable missing semantics and produce robust multimodal representations. Furthermore, we introduce a joint proxy learning module that assists IMDR in eliminating intra-modality redundancy by exploiting the extracted proxies from each class. Experiments on four ophthalmology multimodal datasets demonstrate that the proposed IMDR outperforms the state-of-the-art methods significantly.

A. I. Medvedeva, V. V. Bakutkin

Subject of research: is the study of methods for analyzing perimetric images for the diagnosis and control of glaucoma diseases. Objects of research: is a dataset collected on the ophthalmological perimeter with the results of various patient pathologies, since the ophthalmological community is acutely aware of the issue of disease control and import substitution. [5]. Purpose of research: is to consider various machine learning methods that can classify glaucoma. This is possible thanks to the classifier built after labeling the dataset. It is able to determine from the image whether the visual fields depicted on it are the results of the impact of glaucoma on the eyes or other visual diseases. Earlier in the work [3], a dataset was described that was collected on the Tomey perimeter. The average age of the examined patients ranged from 30 to 85 years. Methods of research: machine learning methods for classifying image results (stochastic gradient descent, logistic regression, random forest, naive Bayes). Main results of research: the result of the study is computer modeling that can determine from the image whether the result is glaucoma or another disease (binary classification).

Rabah Dabouz, Pénélope Abram, Jose Carlos Rivera et al.

Abstract ‘Wet’ age-related macular degeneration (AMD) is characterized by pathologic choroidal neovascularization (CNV) that destroys central vision. Abundant evidence points to inflammation and immune cell dysfunction in the progression of CNV in AMD. Mast cells are resident immune cells that control the inflammatory response. Mast cells accumulate and degranulate in the choroid of patients with AMD, suggesting they play a role in CNV. Activated mast cells secrete various biologically active mediators, including inflammatory cytokines and proteolytic enzymes such as tryptase. We investigated the role of mast cells in AMD using a model of CNV. Conditioned media from activated mast cells exerts proangiogenic effects on choroidal endothelial cells and choroidal explants. Laser-induced CNV in vivo was markedly attenuated in mice genetically depleted of mast cells (KitW−sh/W−sh) and in wild-type mice treated with mast cell stabilizer, ketotifen fumarate. Tryptase was found to elicit pronounced choroidal endothelial cell sprouting, migration and tubulogenesis; while tryptase inhibition diminished CNV. Transcriptomic analysis of laser-treated RPE/choroid complex revealed collagen catabolism and extracellular matrix (ECM) reorganization as significant events correlated in clusters of mast cell activation. Consistent with these analyses, compared to wildtype mice choroids of laser-treated mast cell-deficient mice displayed less ECM remodelling evaluated using collagen hybridizing peptide tissue binding. Findings herein provide strong support for mast cells as key players in the progression of pathologic choroidal angiogenesis and as potential therapeutic targets to prevent pathological neovascularization in ‘wet’ AMD.

Mikhail Kulyabin, Aline Sindel, Hilde Pedersen et al.

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labelling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labelled dataset of 20 AOSLO batches of images of 18 participants for 0$^{\circ}$, 1$^{\circ}$, and 2$^{\circ}$ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0$^{\circ}$, 1$^{\circ}$, and 2$^{\circ}$, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labelled data by only necessitating a fraction of labelled cones, which is especially beneficial in the field of ophthalmology, where labelled data can often be limited.

Stefan Ploner, Jungeun Won, Julia Schottenhamml et al.

Optical coherence tomography (OCT) is a non-invasive, micrometer-scale imaging modality that has become a clinical standard in ophthalmology. By raster-scanning the retina, sequential cross-sectional image slices are acquired to generate volumetric data. In-vivo imaging suffers from discontinuities between slices that show up as motion and illumination artifacts. We present a new illumination model that exploits continuity in orthogonally raster-scanned volume data. Our novel spatiotemporal parametrization adheres to illumination continuity both temporally, along the imaged slices, as well as spatially, in the transverse directions. Yet, our formulation does not make inter-slice assumptions, which could have discontinuities. This is the first optimization of a 3D inverse model in an image reconstruction context in OCT. Evaluation in 68 volumes from eyes with pathology showed reduction of illumination artifacts in 88\% of the data, and only 6\% showed moderate residual illumination artifacts. The method enables the use of forward-warped motion corrected data, which is more accurate, and enables supersampling and advanced 3D image reconstruction in OCT.

Jing Wang, Junyan Fan, Meng Zhou et al.

Ultrasound imaging reveals eye morphology and aids in diagnosing and treating eye diseases. However, interpreting diagnostic reports requires specialized physicians. We present a labeled ophthalmic dataset for the precise analysis and the automated exploration of medical images along with their associated reports. It collects three modal data, including the ultrasound images, blood flow information and examination reports from 2,417 patients at an ophthalmology hospital in Shenyang, China, during the year 2018, in which the patient information is de-identified for privacy protection. To the best of our knowledge, it is the only ophthalmic dataset that contains the three modal information simultaneously. It incrementally consists of 4,858 images with the corresponding free-text reports, which describe 15 typical imaging findings of intraocular diseases and the corresponding anatomical locations. Each image shows three kinds of blood flow indices at three specific arteries, i.e., nine parameter values to describe the spectral characteristics of blood flow distribution. The reports were written by ophthalmologists during the clinical care. The proposed dataset is applied to generate medical report based on the cross-modal deep learning model. The experimental results demonstrate that our dataset is suitable for training supervised models concerning cross-modal medical data.

Md Abdul Kadir, Hasan Md Tusfiqur Alam, Pascale Maul et al.

Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging and disease diagnosis. However, manually annotating a lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL) based segmentation algorithms have completely transformed this process and made it possible to automate image segmentation. By accurately segmenting medical images, these algorithms can greatly minimize the time and effort necessary for manual annotation. Additionally, by incorporating Active Learning (AL) methods, these segmentation algorithms can perform far more effectively with a smaller amount of ground truth data. We introduce MedDeepCyleAL, an end-to-end framework implementing the complete AL cycle. It provides researchers with the flexibility to choose the type of deep learning model they wish to employ and includes an annotation tool that supports the classification and segmentation of medical images. The user-friendly interface allows for easy alteration of the AL and DL model settings through a configuration file, requiring no prior programming experience. While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project.

Barbara Skerlavaj, Gerard Boix-Lemonche

Due to the well-known phenomenon of antibiotic resistance, there is a constant need for antibiotics with novel mechanisms and different targets respect to those currently in use. In this regard, the antimicrobial peptides (AMPs) seem very promising by virtue of their bactericidal action, based on membrane permeabilization of susceptible microbes. Thanks to this feature, AMPs have a broad activity spectrum, including antibiotic-resistant strains, and microbial biofilms. Additionally, several AMPs display properties that can help tissue regeneration. A possible interesting field of application for AMPs is the development of antimicrobial coatings for implantable medical devices (e.g., orthopaedic prostheses) to prevent device-related infection. In this review, we will take note of the state of the art of AMP-based coatings for orthopaedic prostheses. We will review the most recent studies by focusing on covalently linked AMPs to titanium, their antimicrobial efficacy and plausible mode of action, and cytocompatibility. We will try to extrapolate some general rules for structure–activity (orientation, density) relationships, in order to identify the most suitable physical and chemical features of peptide candidates, and to optimize the coupling strategies to obtain antimicrobial surfaces with improved biological performance.

Yi-Ming Zhao, Rong-Sha Sun, Fang Duan et al.

AIM: To evaluate the effects of intravitreal slow-release dexamethasone on traumatic proliferative vitreoretinopathy (PVR) and Müller cell gliosis and preliminarily explored the possible inflammatory mechanism in a rabbit model induced by penetrating ocular trauma. METHODS: Traumatic PVR was induced in the right eyes of pigmented rabbits by performing an 8-mm circumferential scleral incision placed 2.5 mm behind the limbus, followed by treatment with a slow-release dexamethasone implant (Ozurdex) or sham injection. Left eyes were used as normal controls. The intraocular pressure (IOP) was monitored using an iCare tonometer. PVR severity was evaluated via anatomical and histopathological examinations every week for 6wk; specific inflammatory cytokine and proliferative marker levels were measured by quantitative real-time polymerase chain reaction, Western blot, protein chip analysis, or immunofluorescence staining. RESULTS: During the observation period, PVR severity gradually increased. Intense Müller cell gliosis was observed in the peripheral retina near the wound and in the whole retina of PVR group. Ozurdex significantly alleviated PVR development and Müller cell gliosis. Post-traumatic inflammation fluctuated and was persistent. The interleukin-1β (IL-1β) mRNA level was significantly upregulated, peaking on day 3 and increasing again on day 21 after injury. The expression of nod-like receptor family pyrin domain containing 3 (NLRP3) showed a similar trend that began earlier than that of IL-1β expression. Ozurdex suppressed the expression of IL-1β, NLRP3, and phosphorylated nuclear factor-kappa B (NF-κB). The average IOP after treatment was within normal limits. CONCLUSION: The present study demonstrates chronic and fluctuating inflammation in a traumatic PVR rabbit model over 6wk. Ozurdex treatment significantly inhibites inflammatory cytokines expression and Müller cell gliosis, and thus alleviates PVR severity. This study highlights the important role of IL-1β, and Ozurdex inhibites inflammation presumably via the NF-κB/NLRP3/IL-1β inflammatory axis. In summary, Ozurdex provides a potential therapeutic option for traumatic PVR.

Xiaoke Wu, Mengmeng Shi, Yajun Lian et al.

Liquid biopsy strategies enable the noninvasive detection of changes in the levels of circulating biomarkers in body fluid samples, providing an opportunity to diagnose, dynamically monitor, and treat a range of diseases, including cancers. Glioma is among the most common forms of intracranial malignancy, and affected patients exhibit poor prognostic outcomes. As such, diagnosing and treating this disease in its early stages is critical for optimal patient outcomes. Exosomal circular RNAs (circRNAs) are involved in both the onset and progression of glioma. Both the roles of exosomes and methods for their detection have received much attention in recent years and the detection of exosomal circRNAs by liquid biopsy has significant potential for monitoring dynamic changes in glioma. The present review provides an overview of the circulating liquid biopsy biomarkers associated with this cancer type and the potential application of exosomal circRNAs as tools to guide the diagnosis, treatment, and prognostic evaluation of glioma patients during disease progression.

Christian Hoffmann, Christopher Mohr, Patricia Johansson et al.

Abstract We systematically analyzed the kinetics of tumor regression, the impact of residual lesions on disease control and the applicability of the Lugano classification in follow-up MRI of orbital non-Hodgkin lymphomas that were irradiated with photons. We retrospectively analyzed a total of 154 pre- and post-irradiation MRI datasets of 36 patients with low-grade, Ann-Arbor stage I, orbital non-Hodgkin lymphomas. Patients with restricted conjunctival involvement were excluded. Lymphoma lesions were delineated and volumetrically analyzed on T1-weighted sequences. Tumor residues were present in 91.2% of all cases during the first six months after treatment. Volumetric partial response rates (> 50% volume reduction) were 75%, 69.2%, and 50% at 12–24 months, 36–48 months and > 48 months after the end of treatment. The corresponding complete response (CR) rates according to the Lugano classification were 20%, 23.1% and 50%. During a median clinical follow-up of 37 months no significant differences in progression free survival (PFS) rates were observed between the CR and non-CR group (p = 0.915). A residual tumor volume below 20% of the pretreatment volume should be expected at long-term follow-up beyond one year after radiotherapy.

Xin Tian, Nantheera Anantrasirichai, Lindsay Nicholson et al.

Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, each presenting unique benefits and limitations. In-vivo OCT offers rapid, non-invasive imaging but can be hampered by clarity issues and motion artifacts. Ex-vivo confocal microscopy provides high-resolution, cellular detailed color images but is invasive and poses ethical concerns and potential tissue damage. To bridge these modalities, we developed a 3D CycleGAN framework for unsupervised translation of in-vivo OCT to ex-vivo confocal microscopy images. Applied to our OCT2Confocal dataset, this framework effectively translates between 3D medical data domains, capturing vascular, textural, and cellular details with precision. This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy. Assessed through quantitative and qualitative evaluations, the 3D CycleGAN framework demonstrates commendable image fidelity and quality, outperforming existing methods despite the constraints of limited data. This non-invasive generation of retinal confocal images has the potential to further enhance diagnostic and monitoring capabilities in ophthalmology. Our source code and OCT2Confocal dataset are available at https://github.com/xintian-99/OCT2Confocal.

Yang Jianlong, Zhang Haoran, Liu Chang et al.

Optical coherence tomography (OCT) is capable of non-destructively obtaining cross-sectional information of samples with micrometer spatial resolution, which plays an important role in ophthalmology and endovascular medicine. Measuring OCT amplitude can obtain three-dimensional structural information of the sample, such as the layered structure of the retina, but is of limited use for functional information such as tissue specificity, blood flow, and mechanical properties. OCT functional imaging techniques based on other optical field properties including phase, polarization state, and wavelength have emerged, such as Doppler OCT, optical coherence elastography, polarization-sensitive OCT, and visible-light OCT. Among them, functional imaging techniques based on dynamic changes of amplitude have significant robustness and complexity advantages, and achieved significant clinical success in label-free blood flow imaging. In addition, dynamic light scattering OCT for 3D blood flow velocity measurement, dynamic OCT with the ability to display label-free tissue/cell specificity, and OCT thermometry for monitoring the temperature field of thermophysical treatments are the frontiers in OCT functional imaging. In this paper, the principles and applications of the above technologies are summarized, the remaining technical challenges are analyzed, and the future development is envisioned.

Agnieszka Cisek, Karolina Korycinska, Leszek Pyziak et al.

Diabetic retinopathy (DR) is a growing health problem worldwide and is a leading cause of visual impairment and blindness, especially among working people aged 20-65. Its incidence is increasing along with the number of diabetes cases, and it is more common in developed countries than in developing countries. Recent research in the field of diabetic retinopathy diagnosis is using advanced technologies, such as analysis of images obtained by ophthalmoscopy. Automatic methods for analyzing eye images based on neural networks, deep learning and image analysis algorithms can improve the efficiency of diagnosis. This paper describes an automatic DR diagnosis method that includes processing and analysis of ophthalmoscopic images of the eye. It uses morphological algorithms to identify the optic disc and lesions characteristic of DR, such as microaneurysms, hemorrhages and exudates. Automated DR diagnosis has the potential to improve the efficiency of early detection of this disease and contribute to reducing the number of cases of diabetes-related visual impairment. The final step was to create an application with a graphical user interface that allowed retinal images taken at cooperating ophthalmology offices to be uploaded to the server. These images were then analyzed using a developed algorithm to make a diagnosis.

Mitchell A. Kirby, I. Pelivanov, Shaozhen Song et al.

Abstract. Optical coherence elastography (OCE) can provide clinically valuable information based on local measurements of tissue stiffness. Improved light sources and scanning methods in optical coherence tomography (OCT) have led to rapid growth in systems for high-resolution, quantitative elastography using imaged displacements and strains within soft tissue to infer local mechanical properties. We describe in some detail the physical processes underlying tissue mechanical response based on static and dynamic displacement methods. Namely, the assumptions commonly used to interpret displacement and strain measurements in terms of tissue elasticity for static OCE and propagating wave modes in dynamic OCE are discussed with the ultimate focus on OCT system design for ophthalmic applications. Practical OCT motion-tracking methods used to map tissue elasticity are also presented to fully describe technical developments in OCE, particularly noting those focused on the anterior segment of the eye. Clinical issues and future directions are discussed in the hope that OCE techniques will rapidly move forward to translational studies and clinical applications.

Adeeb A. Bulkhi, MD, MSc, Ahmad A. Mirza, MBBS, MSc, Abdullah J. Aburiziza, MD, ABP et al.

Allergic fungal rhinosinusitis (AFRS) is a highly resistant disease and is challenging to treat. Patients with recurrent attacks of the disease despite surgical management can benefit from biologics as adjunct therapies. Dupilumab has shown promising endpoints in patients with chronic rhinosinusitis with nasal polyposis (CRSwNP). This case series reports 4 patients with resistant AFRS concomitant with asthma, for which dupilumab therapy was administered. Long-term follow-ups showed that dupilumab improved the symptoms and improved the results of objective tools such as imaging and pulmonary function test.

Rui-Juan Guan, Xin Yan, Ling Li et al.

AIM: To evaluate the association of complement factor H (CFH) and microtubule-associated protein 1 light chain 3 beta (MAP1LC3B) gene polymorphisms with the risk of age-related macular degeneration (AMD) in a high-altitude population. METHODS: The study group consisted of 172 participants with symptoms of AMD who were examined and diagnosed between January 2019 and June 2020. The control group was composed of 120 healthy individuals. Each participant was required to provide two milliliters of peripheral blood for DNA extraction. Two single nucleotide polymorphisms (SNPs) of CFH (rs1061170 and rs800292) and two SNPs of MAP1LC3B (rs8044820 and rs9903) were genotyped. The genotypes and allele frequencies of the SNPs in the study and control groups were further compared using Chi-square and Fisher's exact tests. RESULTS: In a high-altitude population, the nominally significant differences of rs800292 and rs9903's genotype AG frequencies were observed in the AMD group (P=0.034 and 0.004, respectively). The frequencies of allele G of rs800292 and allele A of rs9903 were also significantly different in the AMD group compared to the control [(P=0.034, OR=0.70, 95%CI: 0.50-0.98) and (P=0.004, OR=1.60, 95%CI: 1.15-2.22), respectively]. No significant differences in the genotype distributions (P=0.16 and 0.40, respectively) and allele frequencies (P>0.05) of rs1061170 and rs8044820 were observed in the AMD group. CONCLUSION: Genotype AG of rs800292 may be a protective factor for AMD. Conversely, rs9903 seems to be a risk factor for AMD. Therefore, allele G of rs800292 may be a protective factor, and allele A of rs9903, a risk factor for AMD in Qinghai high-altitude population.