AIMC Topic: Retinal Drusen
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Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.
Scientific reports
May 16, 2025
Retinal diseases recognition is still a challenging task. Many deep learning classification methods and their modifications have been developed for medical imaging. Recently, Vision Transformers (ViT) have been applied for classification of retinal d...
A comprehensive review on early detection of drusen patterns in age-related macular degeneration using deep learning models.
Photodiagnosis and photodynamic therapy
Dec 22, 2024
Age-related Macular Degeneration (AMD) is a leading cause of visual impairment and blindness that affects the eye from the age of fifty-five and older. It impacts on the retina, the light-sensitive layer of the eye. In early AMD, yellowish deposits c...
Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.
Scientific reports
Nov 15, 2024
To examine the morphological impact of deep learning (DL)-quantified biomarkers on point-wise sensitivity (PWS) using microperimetry (MP) and optical coherence tomography (OCT) in intermediate AMD (iAMD). Patients with iAMD were examined by OCT (Spec...
Self-supervised based clustering for retinal optical coherence tomography images.
Eye (London, England)
Oct 28, 2024
BACKGROUND: In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented.
Validation of a deep learning model for automatic detection and quantification of five OCT critical retinal features associated with neovascular age-related macular degeneration.
The British journal of ophthalmology
Sep 20, 2024
PURPOSE: To develop and validate a deep learning model for the segmentation of five retinal biomarkers associated with neovascular age-related macular degeneration (nAMD).
[Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration].
Die Ophthalmologie
Jul 31, 2024
Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manif...
Machine Teaching Allows for Rapid Development of Automated Systems for Retinal Lesion Detection From Small Image Datasets.
Ophthalmic surgery, lasers & imaging retina
May 1, 2024
Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerging ocular biomarkers from small imaging datasets. We sought to use machine teaching to automaticall...
DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration.
Acta ophthalmologica
Feb 12, 2024
OBJECTIVE: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP).
Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning.
Eye (London, England)
Aug 3, 2021
BACKGROUND: Retinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screenin...
Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning.
American journal of ophthalmology
Jan 8, 2021
PURPOSE: We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD).
Intravitreal Injections
Male
Recombinant Fusion Proteins
Aged
Middle Aged
Wet Macular Degeneration
Choroidal Neovascularization
Ranibizumab
ROC Curve
Humans
Aged, 80 and over
Deep Learning
Geographic Atrophy
Receptors, Vascular Endothelial Growth Factor
Visual Acuity
Angiogenesis Inhibitors
Neural Networks, Computer
Sensitivity and Specificity
Retinal Drusen
Tomography, Optical Coherence
Models, Statistical
Female
Vascular Endothelial Growth Factor A
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