Relationship Between Retinal Layer Thickness and Genetic Susceptibility to Age-Related Macular Degeneration in Asian Populations

Cheong, Kai Xiong; Li, Hengtong; Tham, Yih Chung; Teo, Kelvin Yi Chong; Tan, Anna Cheng Sim; Schmetterer, Leopold; Wong, Tien Yin; Cheung, Chui Ming Gemmy; Cheng, Ching-Yu; Fan, Qiao

doi:10.1016/j.xops.2023.100396

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Reports have revealed that healthy individuals with genetic susceptibility to AMD, estimated through single nucleotide polymorphism and AMD polygenic risk score, have been associated with reduced macular thickness of the central retinal area, particularly at the level of the outer segment PR and RPE. This may indicate that the PR and RPE thinning of the central retinal subfield is a risk factor for the development of AMD [89,90]. Other longitudinal studies that analyzed the relationship between retinal thickness and the emergence of AMD over a follow-up period of more than 10 years reported that the thinning of the PR layer followed by the RPE and Bruch's membrane thickening are among the biomarkers for forthcoming AMD development [91].…”

Section: Correlations Between Oct Macular Thickness and Amd Changesmentioning

confidence: 99%

Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers

Iliescu,

Ghita,

Ilie

et al. 2024

Diagnostics

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The imagistic evaluation of non-neovascular age-related macular degeneration (AMD) is crucial for diagnosis, monitoring progression, and guiding management of the disease. Dry AMD, characterized primarily by the presence of drusen and retinal pigment epithelium atrophy, requires detailed visualization of the retinal structure to assess its severity and progression. Several imaging modalities are pivotal in the evaluation of non-neovascular AMD, including optical coherence tomography, fundus autofluorescence, or color fundus photography. In the context of emerging therapies for geographic atrophy, like pegcetacoplan, it is critical to establish the baseline status of the disease, monitor the development and expansion of geographic atrophy, and to evaluate the retina’s response to potential treatments in clinical trials. The present review, while initially providing a comprehensive description of the pathophysiology involved in AMD, aims to offer an overview of the imaging modalities employed in the evaluation of non-neovascular AMD. Special emphasis is placed on the assessment of progression biomarkers as discerned through optical coherence tomography. As the landscape of AMD treatment continues to evolve, advanced imaging techniques will remain at the forefront, enabling clinicians to offer the most effective and tailored treatments to their patients.

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Section: Correlations Between Oct Macular Thickness and Amd Changesmentioning

confidence: 99%

Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers

Iliescu,

Ghita,

Ilie

et al. 2024

Diagnostics

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Non-exudative OCT findings in neovascular AMD

Vidal-Oliver,

Fernández-Avellaneda,

Fragiotta

et al. 2024

Eye

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Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score

Sendecki,

Ledwoń,

Tuszy

et al. 2024

Biomedicines

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Background: Age-related macular degeneration (AMD) is a complex eye disorder with an environmental and genetic origin, affecting millions worldwide. The study aims to explore the association between retinal morphology and the polygenic risk score (PRS) for AMD using fundus images and deep learning techniques. Methods: The study used and pre-processed 23,654 fundus images from 332 subjects (235 patients with AMD and 97 controls), ultimately selecting 558 high-quality images for analysis. The fine-tuned DenseNet121 deep learning model was employed to estimate PRS from single fundus images. After training, deep features were extracted, fused, and used in machine learning regression models to estimate PRS for each subject. The Grad-CAM technique was applied to examine the relationship between areas of increased model activity and the retina’s morphological features specific to AMD. Results: Using the hybrid approach improved the results obtained by DenseNet121 in 5-fold cross-validation. The final evaluation metrics for all predictions from the best model from each fold are MAE = 0.74, MSE = 0.85, RMSE = 0.92, R2 = 0.18, MAPE = 2.41. Grad-CAM heatmap evaluation showed that the model decisions rely on lesion area, focusing mostly on the presence of drusen. The proposed approach was also shown to be sensitive to artifacts present in the image. Conclusions: The findings indicate an association between fundus images and AMD PRS, suggesting that deep learning models may effectively estimate genetic risk for AMD from retinal images, potentially aiding in early detection and personalized treatment strategies.

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Relationship Between Retinal Layer Thickness and Genetic Susceptibility to Age-Related Macular Degeneration in Asian Populations

Cited by 3 publications

References 42 publications

Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers

Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers

Non-exudative OCT findings in neovascular AMD

Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score

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