AIMC Topic: Fundus Oculi
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Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.
Eye (London, England)
Jun 25, 2024
PURPOSE: To provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks.
Protocol for performing deep learning-based fundus fluorescein angiography image analysis with classification and segmentation tasks.
STAR protocols
Jun 19, 2024
Fundus fluorescein angiography (FFA) examinations are widely used in the evaluation of fundus disease conditions to facilitate further treatment suggestions. Here, we present a protocol for performing deep learning-based FFA image analytics with clas...
Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity.
Journal of biomedical optics
Jun 18, 2024
SIGNIFICANCE: Retinopathy of prematurity (ROP) poses a significant global threat to childhood vision, necessitating effective screening strategies. This study addresses the impact of color channels in fundus imaging on ROP diagnosis, emphasizing the ...
RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images.
Journal of imaging informatics in medicine
Jun 14, 2024
Retinal diseases stand as a primary cause of childhood blindness. Analyzing the progression of these diseases requires close attention to lesion morphology and spatial information. Standard image registration methods fail to accurately reconstruct pe...
Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.
Medical & biological engineering & computing
Jun 14, 2024
Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accurate and early diagnosis. Conventionally, fundus images are used as the gold diagnosis standard in detecting retinal diseases. In recent years, more a...
A fundus image dataset for intelligent retinopathy of prematurity system.
Scientific data
May 27, 2024
Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them ...
Autonomous screening for laser photocoagulation in fundus images using deep learning.
The British journal of ophthalmology
May 21, 2024
BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning algorithms has been increasingly used in retinal image analysis, particularly for the screening of r...
Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.
Annals of medicine
May 13, 2024
BACKGROUND: Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with ...
Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.
Eye (London, England)
May 11, 2024
BACKGROUND/OBJECTIVES: Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening po...
Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography.
Scientific reports
May 11, 2024
The non-perfusion area (NPA) of the retina is an important indicator in the visual prognosis of patients with branch retinal vein occlusion (BRVO). However, the current evaluation method of NPA, fluorescein angiography (FA), is invasive and burdensom...
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