The relationship between age and colour content in fundus images

Ege, Bernhard Mogens; Hejlesen, Ole K.; Larsen, Ole Næsbye; Bek, Toke

doi:10.1034/j.1600-0420.2002.800505.x

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Our attention maps highlighted the eye fundus vascular features, which are consistent with Poplin et al’s attention maps (Poplin et al, n.d.). Ege et al reported that with age, eye fundus images tend to become more yellow because of optimal imperfections in the refractive media (Ege et al, 2002), which was possibly leveraged by our neural networks and could explain why some non-vascularized regions were also highlighted. Finally, our attention maps highlighted most retinal layers in the OCT images.…”

Section: Discussionmentioning

confidence: 83%

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Diai

Collin

et al. 2021

Preprint

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With age, eyesight declines and the vulnerability to age-related eye diseases such as glaucoma, cataract, macular degeneration and diabetic retinopathy increases. With the aging of the global population, the prevalence of these diseases is projected to increase, leading to reduced quality of life and increased healthcare cost. In the following, we built an eye age predictor by training convolutional neural networks to predict age from 175,000 eye fundus and optical coherence tomography images (R-Squared=83.6+/-0.6%; root mean squared error=3.34+/-0.07 years). We used attention maps to identify the features driving the eye age prediction. We defined accelerated eye aging as the difference between eye age and chronological age and performed a genome wide association study [GWAS] on this phenotype. Accelerated eye aging is 28.2+-1.2% GWAS-heritable, and is associated with 255 single nucleotide polymorphisms in 122 genes (e.g HERC2, associated with eye pigmentation). Similarly, we identified biomarkers (e.g blood pressure), clinical phenotypes (e.g chest pain), diseases (e.g cataract), environmental variables (e.g sleep deprivation) and socioeconomic variables (e.g income) associated with our newly defined phenotype. Our predictor could be used to detect premature eye aging in patients, and to evaluate the effect of emerging rejuvenation therapies on eye health.

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Section: Discussionmentioning

confidence: 83%

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Diai

Collin

et al. 2021

Preprint

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“… 22 In addition, various structural abnormalities involving the optic disc were found associated with elongated AL, including PPA, 23 tilted disc, and enlarged optic disc crescent. 24 , 25 On the other hand, aging could affect the appearance of the fundus captured by CFP with multiple means, which include influencing the color presented in the photos, altering the retinal microvasculature, increasing the visibility of large choroidal vessels due to tessellation, 11 , 26 and even causing age-related macular degeneration. 27 In addition to AL and age, Korot et al 8 demonstrated that CFP could be used to predict sex via deep learning models, suggesting the presence of sex-related differences in the appearance of the fundus.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the color content of fundus images was found to be shifted during aging. 11 Remarkably, both retinal vascular structures and color content are included in CFP. Due to the age-related changes of CFP, Wang et al 12 developed convolutional neural network (CNN) models by integrating retinal fundus and vessel images as combined data inputs for predicting age, and the results showed commendable performance.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network–Based Prediction of Axial Length Using Color Fundus Photography

Yang,

Chen,

Yeh

et al. 2024

Trans. Vis. Sci. Tech.

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Purpose To develop convolutional neural network (CNN)–based models for predicting the axial length (AL) using color fundus photography (CFP) and explore associated clinical and structural characteristics. Methods This study enrolled 1105 fundus images from 467 participants with ALs ranging from 19.91 to 32.59 mm, obtained at National Taiwan University Hospital between 2020 and 2021. The AL measurements obtained from a scanning laser interferometer served as the gold standard. The accuracy of prediction was compared among CNN-based models with different inputs, including CFP, age, and/or sex. Heatmaps were interpreted by integrated gradients. Results Using age, sex, and CFP as input, the mean ± standard deviation absolute error (MAE) for AL prediction by the model was 0.771 ± 0.128 mm, outperforming models that used age and sex alone (1.263 ± 0.115 mm; P < 0.001) and CFP alone (0.831 ± 0.216 mm; P = 0.016) by 39.0% and 7.31%, respectively. The removal of relatively poor-quality CFPs resulted in a slight MAE reduction to 0.759 ± 0.120 mm without statistical significance ( P = 0.24). The inclusion of age and CFP improved prediction accuracy by 5.59% ( P = 0.043), while adding sex had no significant improvement ( P = 0.41). The optic disc and temporal peripapillary area were highlighted as the focused areas on the heatmaps. Conclusions Deep learning–based prediction of AL using CFP was fairly accurate and enhanced by age inclusion. The optic disc and temporal peripapillary area may contain crucial structural information for AL prediction in CFP. Translational Relevance This study might aid AL assessments and the understanding of the morphologic characteristics of the fundus related to AL.

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“…The retinal microvasculature shares similar anatomical and physiological features with other end organs including the brain, heart and kidneys, and subtle variations in retinal microvascular parameters (RMPs) may reflect similar variation within the cerebral, renal and coronary circulation [12][13][14]. Recent advances in retinal fundus imaging provide a unique and non-invasive assessment of the microvasculature currently not possible elsewhere in the body [15][16][17][18], enabling opportunistic evaluation of vascular disease and the wider systemic circulation [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Association of reduced retinal arteriolar tortuosity with depression in older participants from the Northern Ireland Cohort for the Longitudinal Study of Ageing

O'Neill¹,

Maxwell²,

Kee³

et al. 2021

BMC Geriatr

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Introduction The retina shares similar anatomical and physiological features with the brain and subtle variations in retinal microvascular parameters (RMPs) may reflect similar vascular variation in the brain. The aim of this study was to assess associations between RMPs and measures of depression in the Northern Ireland Cohort for the Longitudinal Study of Ageing. Methods RMPs (arteriolar and venular caliber, fractal dimension and tortuosity) were measured from optic disc centred fundus images using semi-automated software. Depression was characterised by the Centre for Epidemiologic Studies Depression Scale (CES-D) in the absence of mild cognitive impairment or use of anti-depressive medications. Associations between depression and RMPs were assessed by regression analyses with adjustment for potential confounders. Results Data were available for 1376 participants of which 113 (8.2%) and 1263 (91.8%) were classified with and without depression. Participants had a mean age of 62.0 ± 8.4 yrs., 52% were female, and 8% were smokers. Individuals with depression had a higher CES-D score than those without (22.0 ± 6.2 versus 4.4 ± 3.9). Lower values of arteriolar tortuosity were significantly associated with depression, before and after adjustment for potential confounders (odds ratio = 0.79; 95% confidence intervals: 0.65, 0.96; P = 0.02). Conclusion Decreased retinal arteriolar tortuosity, a measure of the complexity of the retinal microvasculature was associated with depression in older adults independent of potential confounding factors. Retinal measures may offer opportunistic assessment of microvascular health associated with outcomes of depression.

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The relationship between age and colour content in fundus images

Cited by 6 publications

References 13 publications

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Deep learning of fundus and optical coherence tomography images enables identification of diverse genetic and environmental factors associated with eye aging

Convolutional Neural Network–Based Prediction of Axial Length Using Color Fundus Photography

Association of reduced retinal arteriolar tortuosity with depression in older participants from the Northern Ireland Cohort for the Longitudinal Study of Ageing

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