Dataset Information

Evaluation of Geographic Atrophy from Color Photographs and Fundus Autofluorescence Images: Age-Related Eye Disease Study 2 Report Number 11.

ABSTRACT: PURPOSE:To compare measurements of area of geographic atrophy (GA) and change in GA area from color photographs and fundus autofluorescence (FAF) images. DESIGN:The Age-Related Eye Disease Study 2 (AREDS2) was a prospective multicenter randomized clinical trial evaluating progression of dry age-related macular degeneration (AMD) using color photographs at annual visits over a 5-year study period. The FAF images were acquired in a subset of participants who joined the FAF ancillary study at any of the annual visits over the study period. PARTICIPANTS:The AREDS2 FAF ancillary study included 8070 corresponding color and FAF visits of 2202 participants with variable follow-up. METHODS:Corresponding color and FAF images were independently evaluated at a central reading center for GA area measurement, lesion growth, and involvement of the macula center. MAIN OUTCOME MEASURES:Presence, area, growth rate of GA, and involvement of center of macula from color and FAF images. RESULTS:Hypoautofluorescence was visible in 2048 visits (25.4%). Agreement for the presence of GA between the 2 modalities had a kappa of 0.79, with 23% of visits with hypoautofluorescence not presenting with GA on color photographs. Percentage agreement for GA presence ranged from 43% at baseline to 81% at year 5 with improving agreement over time. The mean difference in GA area between the 2 modalities was 0.5 mm2, with larger areas on FAF. Growth rate of GA was 1.45 mm2 from color photographs and 1.43 mm2 from FAF images. The center of the macula was involved in 51% of color photographs and 56% with FAF images. CONCLUSIONS:Geographic atrophy may be detected earlier by the use of FAF images, but over the course of the study, the 2 modalities become comparable. Progression of GA area is comparable between color photographs and FAF images, but evaluating involvement of the center of the macula may differ, probably because of macular pigmentation blocking autofluorescence.

SUBMITTER: Domalpally A

PROVIDER: S-EPMC5077673 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Evaluation of Geographic Atrophy from Color Photographs and Fundus Autofluorescence Images: Age-Related Eye Disease Study 2 Report Number 11.

Domalpally Amitha A Danis Ronald R Agrón Elvira E Blodi Barbara B Clemons Traci T Chew Emily E

Ophthalmology 20160719 11

<h4>Purpose</h4>To compare measurements of area of geographic atrophy (GA) and change in GA area from color photographs and fundus autofluorescence (FAF) images.<h4>Design</h4>The Age-Related Eye Disease Study 2 (AREDS2) was a prospective multicenter randomized clinical trial evaluating progression of dry age-related macular degeneration (AMD) using color photographs at annual visits over a 5-year study period. The FAF images were acquired in a subset of participants who joined the FAF ancillary ...[more]

PMID: 27448832

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Project description:PURPOSE:To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA). DESIGN:A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios. PARTICIPANTS:A total of 59?812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol. METHODS:A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD. A second model was trained to predict CGA presence from the same population. A third model was trained to predict CGA presence from the subset of eyes with GA. For training and testing, 5-fold cross-validation was used. For comparison with human clinician performance, model performance was compared with that of 88 retinal specialists. MAIN OUTCOME MEASURES:Area under the curve (AUC), accuracy, sensitivity, specificity, and precision. RESULTS:The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUCs of 0.933-0.976, 0.939-0.976, and 0.827-0.888, respectively. The GA detection model had accuracy, sensitivity, specificity, and precision of 0.965 (95% confidence interval [CI], 0.959-0.971), 0.692 (0.560-0.825), 0.978 (0.970-0.985), and 0.584 (0.491-0.676), respectively, compared with 0.975 (0.971-0.980), 0.588 (0.468-0.707), 0.982 (0.978-0.985), and 0.368 (0.230-0.505) for the retinal specialists. The CGA detection model had values of 0.966 (0.957-0.975), 0.763 (0.641-0.885), 0.971 (0.960-0.982), and 0.394 (0.341-0.448). The centrality detection model had values of 0.762 (0.725-0.799), 0.782 (0.618-0.945), 0.729 (0.543-0.916), and 0.799 (0.710-0.888). CONCLUSIONS:A deep learning model demonstrated high accuracy for the automated detection of GA. The AUC was noninferior to that of human retinal specialists. Deep learning approaches may also be applied to the identification of CGA. The code and pretrained models are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.

Project description:PurposeQuantitative fundus autofluorescence (qAF) and spectral-domain optical coherence tomography (SD OCT) were performed in patients with bull's-eye maculopathy (BEM) to identify phenotypic markers that can aid in the differentiation of ABCA4-associated and non-ABCA4-associated disease.DesignProspective cross-sectional study at an academic referral center.SubjectsThirty-seven BEM patients (age range, 8-60 years) were studied. All patients exhibited a localized macular lesion exhibiting a smooth contour and qualitatively normal-appearing surrounding retina without flecks. Control values consisted of previously published data from 277 healthy subjects (374 eyes; age range, 5-60 years) without a family history of retinal dystrophy.MethodsAutofluorescence (AF) images (30°, 488-nm excitation) were acquired with a confocal scanning laser ophthalmoscope equipped with an internal fluorescent reference to account for variable laser power and detector sensitivity. The grey levels (GLs) from 8 circularly arranged segments positioned at an eccentricity of approximately 7° to 9° in each image were calibrated to the reference (0 GL), magnification, and normative optical media density to yield qAF. In addition, horizontal SD OCT images through the fovea were obtained. All patients were screened for ABCA4 mutations using the ABCR600 microarray, next-generation sequencing, or both.Main outcome measuresQuantitative AF, correlations between AF and SD OCT, and genotyping for ABCA4 variants.ResultsABCA4 mutations were identified in 22 patients, who tended to be younger (mean age, 21.9±8.3 years) than patients without ABCA4 mutations (mean age, 42.1±14.9 years). Whereas phenotypic differences were not obvious on the basis of qualitative fundus AF and SD OCT imaging, with qAF, the 2 groups of patients were clearly distinguishable. In the ABCA4-positive group, 37 of 41 eyes (19 of 22 patients) had qAF8 of more than the 95% confidence interval for age. Conversely, in the ABCA4-negative group, 22 of 26 eyes (13 of 15 patients) had qAF8 within the normal range.ConclusionsThe qAF method can differentiate between ABCA4-associated and non-ABCA4-associated BEM and may guide clinical diagnosis and genetic testing.

Dataset Information

Evaluation of Geographic Atrophy from Color Photographs and Fundus Autofluorescence Images: Age-Related Eye Disease Study 2 Report Number 11.

Publications

Evaluation of Geographic Atrophy from Color Photographs and Fundus Autofluorescence Images: Age-Related Eye Disease Study 2 Report Number 11.

Similar Datasets

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