AIMC Topic: Macular Degeneration

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Assessment of and polymorphisms in age-related macular degeneration using classic and machine-learning approaches.

Ophthalmic genetics
BACKGROUND: and are pivotal genes driving increased risk for age-related macular degeneration (AMD) among several populations. Here, we performed a hospital-based case-control study to evaluate the effects of three single nucleotide polymorphisms (...

Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degeneration.

Genetic epidemiology
Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successfu...

Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.

Scientific reports
Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, replicating human-made decisions may inherently be biased by the fallible and dogmatic nature of hum...

Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.

Ophthalmology
PURPOSE: To evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with age-related macular degeneration (AMD) and compare performance with an artificial intelligence algori...

Age-related Macular Degeneration: Nutrition, Genes and Deep Learning-The LXXVI Edward Jackson Memorial Lecture.

American journal of ophthalmology
PURPOSE: To evaluate the importance of nutritional supplements, dietary pattern, and genetic associations in age-related macular degeneration (AMD); and to discuss the technique of artificial intelligence/deep learning to potentially enhance research...

Deep learning architectures analysis for age-related macular degeneration segmentation on optical coherence tomography scans.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Aged people usually are more to be diagnosed with retinal diseases in developed countries. Retinal capillaries leakage into the retina swells and causes an acute vision loss, which is called age-related macular degeneration...

Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.

Ophthalmology
PURPOSE: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD).

Predicting conversion to wet age-related macular degeneration using deep learning.

Nature medicine
Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the ...

Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression.

American journal of ophthalmology
PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) using deep learning (DL) and investigate the association with local and global growth ...

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration.

Acta ophthalmologica
PURPOSE: To validate the performance of a commercially available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular dege...