Automated analysis of digital fundus autofluorescence images of geographic atrophy in advanced age-related macular degeneration using confocal scanning laser ophthalmoscopy (cSLO)

Deckert, Andreas; Schmitz-Valckenberg, Steffen; Jorzik, Jork J.; Bindewald, A.; Holz, Frank G.; Mansmann, Ulrich

doi:10.1186/1471-2415-5-8

Cited by 68 publications

(46 citation statements)

References 15 publications

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“…An important source of measurement error is the method of quantifying the GA area. 39 The analysis shows substantial random effects. In both models, the greatest source of variation is the interindividual variability (Var(α i ) and Var(α ij )), i.e.…”

Section: Discussionmentioning

confidence: 98%

Modelling the Natural History of Geographic Atrophy in Patients with Age-Related Macular Degeneration

Dreyhaupt

Mansmann

Pritsch

et al. 2005

Ophthalmic Epidemiology

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The natural course of GA varies extremely between individuals. However, reliable factors for the explanation of this variability have so far not been established. MEM are useful for describing "inter-individual" as well as "intra-individual" influences without the need for precise knowledge of the influencing factors. Using MEM to evaluate data on the natural history of GA allows one to derive parameter estimates, which could be used to design interventional trials for modes of therapy with a potential to reduce or stop the progression of GA in patients with AMD.

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Section: Discussionmentioning

confidence: 98%

Modelling the Natural History of Geographic Atrophy in Patients with Age-Related Macular Degeneration

Dreyhaupt

Mansmann

Pritsch

et al. 2005

Ophthalmic Epidemiology

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“…For each visit, the status of the fovea with regard to any atrophy involvement within a circle of 300 mm in diameter centered on the fovea was classified as "foveal" or "extrafoveal" GA. For each visit, the total size of atrophy was measured by a semiautomatic procedure, which has been described in detail. 18,19 Briefly, the reader manually set a seeding point inside the atrophic region to start an automatic region-growing algorithm that detected well-demarcated areas of severely decreased FAF signal. The reader then manually adjusted the threshold of the algorithm.…”

Section: Methodsmentioning

confidence: 99%

Natural History of Geographic Atrophy Progression Secondary to Age-Related Macular Degeneration (Geographic Atrophy Progression Study)

et al. 2016

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“…The total size of uni- or multifocal GA was measured on processed and registered FAF images using customized image analysis software according to predefined grading instructions, as previously described [26,27]. For further analyses, total GA lesion size was square root transformed to reduce the dependency of growth rates on baseline lesion size measurements [√area (mm)] [15,28].…”

Section: Methodsmentioning

confidence: 99%

Modeling Visual Acuity in Geographic Atrophy Secondary to Age-Related Macular Degeneration

Schmitz-Valckenberg¹,

Nadal

Fimmers

et al. 2016

Ophthalmologica

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Purpose: To analyze and model visual acuity (VA) in geographic atrophy (GA) secondary to age-related macular degeneration (AMD). Methods: The course of VA was analyzed using Turnbull's estimator in 226 eyes with uni- or bilateral GA due to AMD (151 patients; mean age 74.0 ± 7.6 years; mean follow-up time 33.4 ± 23.4 months) from the natural history FAM (Fundus-Autofluorescence Imaging in AMD) study. The variables ‘age at baseline', ‘gender', ‘lesion size', ‘diagnosis of the fellow eye', ‘status of the fovea', ‘focality of the lesion' and ‘pattern' were evaluated for effects on predicting VA using linear mixed-effects models. Results: Mean VA at baseline was0.6 (Snellen 20/80) ± 0.4 logMAR [range -0.1 to 1.8 (20/17 to hand motions)], showing an estimated mean increase of 0.181 (95% CI 0.152-0.210) and 0.256 (0.214-0.300) after 2 and 4 years of follow-up, respectively. The percentage of eyes with a loss of ≥3 lines was 34% by 2 years and 47% by 4 years. Linear mixed model analysis suggested that 65% of VA variability could be explained by the assessed predictor variables. The strongest effect was found for the ‘status of the fovea' (0.69 logMAR units between ‘definitively spared fovea' and ‘definitive foveal involvement', p < 0.001). The second strongest effect was identified for ‘total lesion size' (effects between 0.02 and 0.09 logMAR units for each mm depending on foveal involvement, p < 0.001, square root transformed values). Conclusions: These findings underscore the importance of GA lesion characteristics as these have the strongest impact on VA. Natural history data and modeling VA to other variables will be helpful for refining outcome parameters and estimating possible benefits of therapeutic interventions.

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Automated analysis of digital fundus autofluorescence images of geographic atrophy in advanced age-related macular degeneration using confocal scanning laser ophthalmoscopy (cSLO)

Abstract: Background: Fundus autofluorescence (AF) imaging using confocal scanning laser ophthalmoscopy (cSLO) provides an accurate delineation of areas of geographic atrophy (GA).

Cited by 68 publications

References 15 publications

Modelling the Natural History of Geographic Atrophy in Patients with Age-Related Macular Degeneration

Modelling the Natural History of Geographic Atrophy in Patients with Age-Related Macular Degeneration

Natural History of Geographic Atrophy Progression Secondary to Age-Related Macular Degeneration (Geographic Atrophy Progression Study)

Modeling Visual Acuity in Geographic Atrophy Secondary to Age-Related Macular Degeneration

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