Objective: A new diagnostic graphical tool-classification maps-supporting the detection of Age-Related Macular Degeneration (AMD) has been constructed.

Methods: The classification maps are constructed using the ordinal regression model. In the ordinal regression model, the ordinal variable (the dependent variable) is the degree of the advancement of AMD. The other variables, such as CRT (Central Retinal Thickness), GCC (Ganglion Cell Complex), MPOD (Macular Pigment Optical Density), ETDRS (Early Treatment Diabetic Retinopathy Study), Snellen and Age have also been used in the analysis and are represented on the axes of the maps.

Results: Here, 132 eyes were examined and classified to the AMD advancement level according to the four-point Age-Related Eye Disease Scale (AREDS): AREDS 1, AREDS 2, AREDS 3 and AREDS 4. These data were used for the creation of two-dimensional classification maps for each of the four stages of AMD.

Conclusions: The maps allow us to perform the classification of the patient's eyes to particular stages of AMD. The pairs of the variables represented on the axes of the maps can be treated as diagnostic identifiers necessary for the classification to particular stages of AMD.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381320PMC
http://dx.doi.org/10.3390/jpm13071074DOI Listing

Publication Analysis

Top Keywords

areds areds
16
classification maps
12
age-related macular
8
macular degeneration
8
ordinal regression
8
regression model
8
model ordinal
8
represented axes
8
stages amd
8
classification
5

Similar Publications

Chronic ocular pain impacts quality of life and is often linked to ocular surgery. We assessed the prevalence of chronic postoperative pain (CPOP) after cataract surgery and associated risk factors using a secondary cohort post-hoc analysis of data from the Age-Related Eye Disease Study (AREDS), a multicenter, controlled, randomized clinical trial of antioxidant vitamins and minerals. Ocular pain was determined from item 4 of the National Eye Institute Visual Function Questionnaire (NEI-VFQ-25), administered between 1997 and 2005.

View Article and Find Full Text PDF

The eye is considered to be an immune-privileged region. However, several parts of the eye have distinct mechanisms for delivering immune cells to the injury sites or even in response to aging. Although these immune responses are intended to be protective, the visual acuity can be compromised by the release of pro-inflammatory cytokines by immune cells, which induce chronic inflammation and fibrosis.

View Article and Find Full Text PDF

Nutritional Genomics: Implications for Age-Related Macular Degeneration.

Nutrients

November 2024

Ophthalmology Department, Unidade Local de Saúde Coimbra, 3004-561 Coimbra, Portugal.

Age-related macular degeneration (AMD) is a leading cause of vision loss in older individuals, driven by a multifactorial etiology involving genetic, environmental, and dietary factors. Nutritional genomics, which studies gene-nutrient interactions, has emerged as a promising field for AMD prevention and management. Genetic predispositions, such as variants in , , , , and oxidative stress pathways, significantly affect the risk and progression of AMD.

View Article and Find Full Text PDF

A deep learning approach for the screening of referable age-related macular degeneration - Model development and external validation.

J Formos Med Assoc

December 2024

Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan; Department of Ophthalmology, School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan. Electronic address:

Purpose: To develop a deep learning image assessment software, VeriSee™ AMD, and to validate its accuracy in diagnosing referable age-related macular degeneration (AMD).

Methods: For model development, a total of 6801 judgable 45-degree color fundus images from patients, aged 50 years and over, were collected. These images were assessed for AMD severity by ophthalmologists, according to the Age-Related Eye Disease Studies (AREDS) AMD category.

View Article and Find Full Text PDF

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence , neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!