scholarly journals Atrophy Expansion Rates in Stargardt Disease Using Ultra-Widefield Fundus Autofluorescence

2021 ◽  
Vol 1 (1) ◽  
pp. 100005
Author(s):  
Rachael C. Heath Jeffery ◽  
Jennifer A. Thompson ◽  
Johnny Lo ◽  
Tina M. Lamey ◽  
Terri L. McLaren ◽  
...  
Keyword(s):  
Fundus Autofluorescence ◽  
Stargardt Disease

scholarly journals Progression of Stargardt Disease as Determined by Fundus Autofluorescence in the Retrospective Progression of Stargardt Disease Study (ProgStar Report No. 9)

2017 ◽  
Vol 135 (11) ◽  
pp. 1232 ◽  
Author(s):  
Rupert W. Strauss ◽  
Beatriz Muñoz ◽  
Alexander Ho ◽  
Anamika Jha ◽  
Michel Michaelides ◽  
...  
Keyword(s):  
Fundus Autofluorescence ◽  
Stargardt Disease ◽  
Disease Study

scholarly journals Fundus Autofluorescence Patterns in Stargardt Disease Over Time—Reply

2012 ◽  
Vol 130 (10) ◽  
pp. 1354 ◽  
Author(s):  
Catherine A. Cukras ◽  
Wai T. Wong ◽  
Rafael Caruso ◽  
Denise Cunningham ◽  
Wadih Zein ◽  
...  
Keyword(s):  
Fundus Autofluorescence ◽  
Stargardt Disease ◽  
Over Time

scholarly journals Fundus autofluorescence, spectral‐domain optical coherence tomography, and histology correlations in a Stargardt disease mouse model

2020 ◽  
Vol 34 (3) ◽  
pp. 3693-3714 ◽  
Author(s):  
Yuan Fang ◽  
Alexander Tschulakow ◽  
Tatjana Taubitz ◽  
Barbara Illing ◽  
Antje Biesemeier ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Mouse Model ◽  
Fundus Autofluorescence ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Stargardt Disease

scholarly journals Abnormal Visual Function Outside the Area of Atrophy Defined by Short-Wavelength Fundus Autofluorescence in Stargardt Disease

2020 ◽  
Vol 61 (4) ◽  
pp. 36 ◽  
Author(s):  
Janet S. Sunness ◽  
Abraham Ifrah ◽  
Robert Wolf ◽  
Carol A. Applegate ◽  
Janet R. Sparrow
Keyword(s):  
Visual Function ◽  
Short Wavelength ◽  
Fundus Autofluorescence ◽  
Stargardt Disease ◽  
Abnormal Visual

Different Patterns of Fundus Autofluorescence Related to ABCA4 Gene Mutationsin Stargardt Disease

2010 ◽  
Vol 41 (1) ◽  
pp. 48-53 ◽  
Author(s):  
Andrea Sodi ◽  
Alessandro Bini ◽  
Ilaria Passerini ◽  
Simona Forconi ◽  
Ugo Menchini ◽  
...  
Keyword(s):  
Fundus Autofluorescence ◽  
Stargardt Disease ◽  
Abca4 Gene

scholarly journals Distinct Characteristics of Inferonasal Fundus Autofluorescence Patterns in Stargardt Disease and Retinitis Pigmentosa

2013 ◽  
Vol 54 (10) ◽  
pp. 6820 ◽  
Author(s):  
Tobias Duncker ◽  
Winston Lee ◽  
Stephen H. Tsang ◽  
Jonathan P. Greenberg ◽  
Jana Zernant ◽  
...  
Keyword(s):  
Retinitis Pigmentosa ◽  
Fundus Autofluorescence ◽  
Stargardt Disease

scholarly journals Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jason Charng ◽  
Di Xiao ◽  
Maryam Mehdizadeh ◽  
Mary S. Attia ◽  
Sukanya Arunachalam ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vision Loss ◽  
Fundus Autofluorescence ◽  
Retinal Disease ◽  
Diagnostic Feature ◽  
Stargardt Disease ◽  
Inherited Retinal Disease ◽  
Trial Endpoint ◽  
Disease Testing

Abstract Stargardt disease is one of the most common forms of inherited retinal disease and leads to permanent vision loss. A diagnostic feature of the disease is retinal flecks, which appear hyperautofluorescent in fundus autofluorescence (FAF) imaging. The size and number of these flecks increase with disease progression. Manual segmentation of flecks allows monitoring of disease, but is time-consuming. Herein, we have developed and validated a deep learning approach for segmenting these Stargardt flecks (1750 training and 100 validation FAF patches from 37 eyes with Stargardt disease). Testing was done in 10 separate Stargardt FAF images and we observed a good overall agreement between manual and deep learning in both fleck count and fleck area. Longitudinal data were available in both eyes from 6 patients (average total follow-up time 4.2 years), with both manual and deep learning segmentation performed on all (n = 82) images. Both methods detected a similar upward trend in fleck number and area over time. In conclusion, we demonstrated the feasibility of utilizing deep learning to segment and quantify FAF lesions, laying the foundation for future studies using fleck parameters as a trial endpoint.

scholarly journals A Comparison of Fundus Autofluorescence and Retinal Structure in Patients with Stargardt Disease

2009 ◽  
Vol 50 (8) ◽  
pp. 3953 ◽  
Author(s):  
Nuno L. Gomes ◽  
Vivienne C. Greenstein ◽  
Joshua N. Carlson ◽  
Stephen H. Tsang ◽  
R. Theodore Smith ◽  
...  
Keyword(s):  
Fundus Autofluorescence ◽  
Stargardt Disease ◽  
Retinal Structure

COMPARISON OF MANUAL AND SEMIAUTOMATED FUNDUS AUTOFLUORESCENCE ANALYSIS OF MACULAR ATROPHY IN STARGARDT DISEASE PHENOTYPE

Retina ◽  
2016 ◽  
Vol 36 (6) ◽  
pp. 1216-1221 ◽  
Author(s):  
Laura Kuehlewein ◽  
Amir H. Hariri ◽  
Alexander Ho ◽  
Laurie Dustin ◽  
Yulia Wolfson ◽  
...  
Keyword(s):  
Fundus Autofluorescence ◽  
Disease Phenotype ◽  
Stargardt Disease ◽  
Macular Atrophy

scholarly journals Absence of Genotype/Phenotype Correlations Requires Molecular Diagnostic to Ascertain Stargardt and Stargardt-Like Swiss Patients

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 812
Author(s):  
Virginie M.M. Buhler ◽  
Lieselotte Berger ◽  
André Schaller ◽  
Martin S. Zinkernagel ◽  
Sebastian Wolf ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Fundus Autofluorescence ◽  
Molecular Diagnostic ◽  
Stargardt Disease ◽  
Vitamin A Supplementation ◽  
Clinical Phenotypes ◽  
Number Of Patients ◽  
Increased Risk ◽  
Swiss Population

We genetically characterized 22 Swiss patients who had been diagnosed with Stargardt disease after clinical examination. We identified in 11 patients (50%) pathogenic bi-allelic ABCA4 variants, c.1760+2T>C and c.4496T>C being novel. The dominantly inherited pathogenic ELOVL4 c.810C>G p.(Tyr270*) and PRPH2-c.422A>G p.(Tyr141Cys) variants were identified in eight (36%) and three patients (14%), respectively. All patients harboring the ELOVL4 c.810C>G p.(Tyr270*) variant originated from the same small Swiss area, identifying a founder mutation. In the ABCA4 and ELOVL4 cohorts, the clinical phenotypes of “flecks”, “atrophy”, and “bull”s eye like” were observed by fundus examination. In the small number of patients harboring the pathogenic PRPH2 variant, we could observe both “flecks” and “atrophy” clinical phenotypes. The onset of disease, progression of visual acuity and clinical symptoms, inheritance patterns, fundus autofluorescence, and optical coherence tomography did not allow discrimination between the genetically heterogeneous Stargardt patients. The genetic heterogeneity observed in the relatively small Swiss population should prompt systematic genetic testing of clinically diagnosed Stargardt patients. The resulting molecular diagnostic is required to prevent potentially harmful vitamin A supplementation, to provide genetic counseling with respect to inheritance, and to schedule appropriate follow-up visits in the presence of increased risk of choroidal neovascularization.

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