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

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

2020 ◽  
Vol 34 (3) ◽  
pp. 3693-3714 ◽  
Author(s):  
Yuan Fang ◽  
Alexander Tschulakow ◽  
Tatjana Taubitz ◽  
Barbara Illing ◽  
Antje Biesemeier ◽  
...  

2020 ◽  
pp. bjophthalmol-2020-316201 ◽  
Author(s):  
Maximilian Pfau ◽  
Frank G. Holz ◽  
Philipp L. Müller

Background/aimsTo evaluate the applicability of mesopic light sensitivity measurements obtained by fundus-controlled perimetry (FCP, also termed ‘microperimetry’) as clinical trial endpoint in Stargardt disease (STGD1).MethodsIn this retrospective, monocentre cohort study, 271 eyes of 136 patients (age, 37.1 years) with STGD1 and 87 eyes of 54 healthy controls (age, 41.0 years) underwent mesopic FCP, using a pattern of 50 stimuli (achromatic, 400–800 nm) centred on the fovea. The concurrent validity of mesopic FCP testing using the MAIA device (CenterVue, Italy), the retest variability and its determinants, and the progression of sensitivity loss over time were investigated using mixed-model analyses. The main outcomes were the average pointwise sensitivity loss in dependence of patients’ demographic, functional and imaging characteristics, the intrasession 95% coefficient of repeatability, and the pointwise sensitivity loss over time.ResultsPointwise sensitivity loss was on average (estimate (95% CI)) 13.88 dB (12.55 to 15.21) along the horizontal meridian and was significantly associated with the electrophysiological subgroup, presence/absence of foveal sparing, best-corrected visual acuity and disease duration. The 95% coefficient of repeatability was 12.15 dB (10.78 to 13.38) and varied in dependence of the underlying mean sensitivity and local sensitivity slope. The global progression rate for the sensitivity loss was 0.45 dB/year (0.13 to 0.78) and was higher for the central and inner ETDRS subfields compared with more peripheral regions.ConclusionsMesopic light sensitivity measured by FCP is reliable and susceptible for functional changes. It constitutes a potential clinical outcome for both natural history studies as well as future interventional studies in patients with STGD1.


2020 ◽  
Vol 61 (4) ◽  
pp. 36 ◽  
Author(s):  
Janet S. Sunness ◽  
Abraham Ifrah ◽  
Robert Wolf ◽  
Carol A. Applegate ◽  
Janet R. Sparrow

2010 ◽  
Vol 41 (1) ◽  
pp. 48-53 ◽  
Author(s):  
Andrea Sodi ◽  
Alessandro Bini ◽  
Ilaria Passerini ◽  
Simona Forconi ◽  
Ugo Menchini ◽  
...  

2013 ◽  
Vol 54 (10) ◽  
pp. 6820 ◽  
Author(s):  
Tobias Duncker ◽  
Winston Lee ◽  
Stephen H. Tsang ◽  
Jonathan P. Greenberg ◽  
Jana Zernant ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jason Charng ◽  
Di Xiao ◽  
Maryam Mehdizadeh ◽  
Mary S. Attia ◽  
Sukanya Arunachalam ◽  
...  

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.


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