AIMC Topic: Fundus Oculi
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Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.
Ophthalmology. Retina
Oct 24, 2024
PURPOSE: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.
Algorithms
Prospective Studies
Female
Aged
Humans
Diabetic Retinopathy
Fundus Oculi
Macular Edema
Fluorescein Angiography
Macula Lutea
Aged, 80 and over
Retinal Neovascularization
Cross-Sectional Studies
Retinal Vein Occlusion
Middle Aged
Male
Deep Learning
Visual Acuity
Tomography, Optical Coherence
Searching Discriminative Regions for Convolutional Neural Networks in Fundus Image Classification With Genetic Algorithms.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Oct 22, 2024
Deep convolutional neural networks (CNNs) have been widely used for fundus image classification and have achieved very impressive performance. However, the explainability of CNNs is poor because of their black-box nature, which limits their applicati...
A multi-class fundus disease classification system based on an adaptive scale discriminator and hybrid loss.
Computational biology and chemistry
Oct 11, 2024
Fundus images are crucial in the observation and detection of ophthalmic diseases. However, detecting multiple ophthalmic diseases from fundus images using deep learning techniques is a complex and challenging task One challenge is the complexity of ...
Artificial intelligence methods in diagnosis of retinoblastoma based on fundus imaging: a systematic review and meta-analysis.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Sep 18, 2024
BACKGROUND: Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about...
Evaluation of AI-enhanced non-mydriatic fundus photography for diabetic retinopathy screening.
Photodiagnosis and photodynamic therapy
Sep 7, 2024
OBJECTIVE: To assess the feasibility of using non-mydriatic fundus photography in conjunction with an artificial intelligence (AI) reading platform for large-scale screening of diabetic retinopathy (DR).
Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.
BMJ open
Sep 5, 2024
OBJECTIVES: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however,...
SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.
Interdisciplinary sciences, computational life sciences
Sep 2, 2024
As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor refle...
Artificial intelligence-based extraction of quantitative ultra-widefield fluorescein angiography parameters in retinal vein occlusion.
Canadian journal of ophthalmology. Journal canadien d'ophtalmologie
Aug 31, 2024
OBJECTIVE: To examine the association between quantitative vascular parameters extracted from intravenous fluorescein angiography (IVFA) and baseline clinical characteristics of patients with retinal vein occlusion (RVO).
Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Aug 28, 2024
Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophth...
Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.
Computer methods and programs in biomedicine
Aug 23, 2024
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...
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