Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naive Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.

Intraocular tuberculosis presents a diagnostic challenge because of its protean clinical manifestations. Thus, it becomes imperative to distinguish chorioretinal inflammation due to tuberculosis from other infectious as well as non-infectious posterior uveitis since the clinical manifestations of these entities may be quite similar. With advancing technology, a number of novel imaging modalities are available today that enable in vivo near-histologic assessment of the chorioretinal tissue, greatly aiding the clinician in establishing a diagnosis and monitoring the therapeutic response of tubercular lesions. Techniques such as ultra-wide field fundus photography, fluorescein and indocyanine green angiography, optical coherence tomography and most recently, optical coherence tomography angiography, provide valuable information that helps in advancing our knowledge regarding the pathogenesis and natural history of the disease. The index review describes how these techniques supplement each other, and highlights the relevance of multimodal imaging in the management of intraocular tuberculosis.

Multimodal Imaging in Ocular Tuberculosis

The pathophysiology of dry age-related macular degeneration (AMD) and specifically geographic atrophy (GA) has been linked to the complement cascade. This cascade is part of the innate immune system and is made up of the classical, alternative, and lectin pathways. The pathways comprise a system of plasma and membrane-associated serum proteins that are activated with identification of a nonself entity. A number of these proteins have been implicated in the development and progression of dry AMD. The three pathways converge at C3 and cascade down through C5, making both of these proteins viable targets for the treatment of dry AMD. In addition, there are a number of complement factors, CFB, CFD, CFH, and CFI, which are potential therapeutic targets as well. Several different complement-directed therapeutics are being studied for the treatment of dry AMD with the hope that one of these approaches will emerge as the first approved treatment for GA.摘要: 干性年龄相关性黄斑变性 (AMD), 特别是地图样萎缩 (GA) 的病理生理学与补体级联有关。这种级联反应是先天免疫系统的一部分, 由经典, 替代和凝集素途径构成。这些通路包括一个与血浆和膜相关的血清蛋白系统, 该系统在识别非本体时被激活。其中的许多蛋白与干型AMD的发生和发展有关。这3条通路在C3处汇合, 并通过C5级联, 这两种蛋白为治疗干性AMD的可行靶点。此外, 还有一些补体因子, CFB, CFD, CFH和CFI, 也是潜在的治疗靶点。目前有几种不同的补体导向疗法也正在研究中, 以治疗干性AMD, 希望其中一种成为第一种被批准的GA治疗方法。. 

/pdf/complement-cascade-inhibition-in-geographic-atrophy-a-review-3p0231w4.pdf

Complement cascade inhibition in geographic atrophy: a review

Personalized and point-of-care (POC) diagnoses are critical for ocular physiology and disease diagnosis. Real-time monitoring and continuous sampling abilities of tear fluid and user-friendliness have become the key characteristics for the applied ophthalmic techniques. Fluorescence technologies, as one of the most popular methods that can fulfill the requirements of clinical ophthalmic applications for optical sensing, have been raised and applied for tear sensing and diagnostic platforms in recent decades. Wearable sensors in this case have been increasingly developed for ocular diagnosis. Contact lenses, as one of the commercialized and popular tools for ocular dysfunction, have been developed as a platform for fluorescence sensing in tears diagnostics and real-time monitoring. Numbers of biochemical analytes have been examined through developed fluorescent contact lens sensors, including pH values, electrolytes, glucose, and enzymes. These sensors have been proven for monitoring ocular conditions, enhancing and detecting medical treatments, and tracking efficiency of related ophthalmic surgeries at POC settings. This review summarizes the applied ophthalmic fluorescence sensing technologies in tears for ocular diagnosis and monitoring. In addition, the cooperation of fabricated fluorescent sensor with mobile phone readout devices for diagnosing ocular diseases with specific biomarkers continuously is also discussed. Further perspectives for the developments and applications of fluorescent ocular sensing and diagnosing technologies are also provided.

/pdf/fluorescence-sensing-technologies-for-ophthalmic-diagnosis-2tyq4l5y.pdf

Fluorescence Sensing Technologies for Ophthalmic Diagnosis

Point-of-care diagnosis and personalized treatments are critical in ocular physiology and disease. Continuous sampling of tear fluid for ocular diagnosis is a need for further exploration. Several techniques have been developed for possible ophthalmological applications, from traditional spectroscopies to wearable sensors. Contact lenses are commonly used devices for vision correction, as well as for other therapeutic and cosmetic purposes. They are increasingly being developed into ocular sensors, being used to sense and monitor biochemical analytes in tear fluid, ocular surface temperature, intraocular pressure, and pH value. These sensors have had success in detecting ocular conditions, optimizing pharmaceutical treatments, and tracking treatment efficacy in point-of-care settings. However, there is a paucity of new and effective instrumentation reported in ophthalmology. Hence, this review will summarize the applied ophthalmic technologies for ocular diagnostics and tear monitoring, including both conventional and biosensing technologies. Besides applications of smart readout devices for continuous monitoring, targeted biomarkers are also discussed for the convenience of diagnosis of various ocular diseases. A further discussion is also provided for future aspects and market requirements related to the commercialization of novel types of contact lens sensors.

/pdf/ophthalmic-sensing-technologies-for-ocular-disease-4fmc3cq47f.pdf

Ophthalmic sensing technologies for ocular disease diagnostics

https://applications.emro.who.int/imemrf/Saudi_J_Ophthalmol/Saudi_J_Ophthalmol_2014_28_2_111_116.pdf

Fundus autofluorescence imaging: Fundamentals and clinical relevance

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Brian Perez

Author Tools

Chat about Author

Papers

Fundus autofluorescence imaging: Fundamentals and clinical relevance