AIMC Topic: Macular Degeneration
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A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: A preliminary study.
Acta ophthalmologica
May 18, 2024
PURPOSE: Age-related macular degeneration (AMD) is a complex eye disorder affecting millions worldwide. This article uses deep learning techniques to investigate the relationship between AMD, genetics and optical coherence tomography (OCT) scans.
Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.
Nature communications
Apr 26, 2024
The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which...
A new intelligent system based deep learning to detect DME and AMD in OCT images.
International ophthalmology
Apr 23, 2024
Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images und...
Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.
Medicina (Kaunas, Lithuania)
Mar 23, 2024
Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighti...
Artificial intelligence in age-related macular degeneration: state of the art and recent updates.
BMC ophthalmology
Mar 15, 2024
Age related macular degeneration (AMD) represents a leading cause of vision loss and it is expected to affect 288 million people by 2040. During the last decade, machine learning technologies have shown great potential to revolutionize clinical manag...
Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.
The British journal of ophthalmology
Feb 21, 2024
AIMS: To develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts.
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).
Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.
BMC ophthalmology
Dec 7, 2023
BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF).
Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy.
Scientific reports
Nov 21, 2023
To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patie...
Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).
Scientific reports
Nov 9, 2023
Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records a...
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