Prospective Cohort Study of Childhood-Onset Stargardt Disease: Fundus Autofluorescence Imaging, Progression, Comparison with Adult-Onset Disease, and Disease Symmetry

Georgiou, Michalis; Kane, Thomas M.; Tanna, Preena; Bouzia, Zaina; Singh, Navjit; Kalitzeos, Angelos; Strauss, Rupert W.; Fujinami, Kaoru; Michaelides, Michel

doi:10.1016/j.ajo.2019.11.008

Cited by 47 publications

(61 citation statements)

References 55 publications

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“…However, quantification of poorly demarcated areas of FAF in other inherited retinal disease can be difficult. 53 …”

Section: Resultsmentioning

confidence: 99%

“…However, quantification of poorly demarcated areas of FAF in other inherited retinal disease can be difficult. 53 In conclusion, our detailed investigation of retinal integrity will be important in identifying and monitoring subjects with RPE65-RD in both treatment and clinical trial settings -both in terms of safety, but moreover, in terms of addressing one of the primary clinical objectives of slowing or halting otherwise inexorable retinal degeneration.…”

Section: Discussionmentioning

confidence: 99%

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Retinal Structure inRPE65-Associated Retinal Dystrophy

Kumaran

Georgiou

Bainbridge

et al. 2020

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. RPE65-associated retinal dystrophy (RPE65-RD) is an early onset, progressive, severe retinal dystrophy. We sought to characterize the natural history of retinal degeneration in affected individuals. METHODS. We performed cross-sectional and longitudinal quantitative and qualitative assessments of retinal architecture in RPE65-RD using spectral domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) imaging. Twenty-six subjects (mean age, 14.8 years, range, 5-24 years) with RPE65-RD underwent SD-OCT and FAF imaging, of whom 14 subjects were followed up over time. Foveal thickness (FT), outer nuclear layer thickness (ONLT), ellipsoid zone width (EZW), and ellipsoid zone area (EZA) were calculated where possible. These were correlated with age, best corrected visual acuity (BCVA), and central 30°retinal sensitivity (V 30). Intra-observer agreement, test-retest repeatability, and interocular symmetry were also investigated. RESULTS. We identified structural interocular symmetry, the presence of autofluorescence in 46% (12/26) of subjects, and the presence of foveal hypoplasia (associated with significantly worse BCVA) in 50% of subjects. EZW and EZA were measurable in 67% (35/52) and 37% (19/52) of eyes, respectively, with both demonstrating good agreement on repeated measurement. The annual rate of progression using EZW was −300.63 μm/year, and −1.17 mm 2 /year in EZA. EZW was found to have a statistically significant correlation with BCVA and V 30. CONCLUSIONS. We identified the presence of autofluorescence in half of our subjects, with foveal hypoplasia also noted in half of our cohort. EZW, and to a lesser extent EZA, were robust measures of retinal degeneration and represent valuable metrics to determine the impact of intervention. (ClinicalTrials.gov number NCT02714816.

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“…However, quantification of poorly demarcated areas of FAF in other inherited retinal disease can be difficult. 53 …”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Retinal Structure inRPE65-Associated Retinal Dystrophy

Kumaran

Georgiou

Bainbridge

et al. 2020

Invest. Ophthalmol. Vis. Sci.

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“…The similar trends in the change in fleck count and area over time using both manual and deep learning segmentation suggests that deep learning algorithms may be useful in future studies of retinal fleck pathophysiology. The reduction in fleck count and area in the central 10° disc can be explained by the natural life cycle of flecks evolving into RPE atrophy 9,14,23 . In contrast, there was a general increase in fleck number and size over time in the outer rings as peripheral atrophy generally does not develop until later in the disease course 21,24 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease

Charng

Xiao

Mehdizadeh

et al. 2020

Sci Rep

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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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“…Dazu gehören ein perizentraler Ring erhöhter Autofluoreszenz bei Retinitis pigmentosa oder Zapfen-Stäbchen-Dystrophien [13 -15] (▶ Abb. 3), ein zentrales scharf begrenztes Areal von erhaltenem RPE oder Fotorezeptoren [15 -17] oder die Größe einer scharf begrenzten Atrophiezone [18,19] (▶ Abb. 4).…”

Section: Retinale Bildgebungunclassified

Hereditäre Netzhautdystrophien: Kombination ophthalmologischer Methoden zur Optimierung des Readout

Kellner¹,

Kellner²,

Saleh³

et al. 2020

Klin Monatsbl Augenheilkd

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ZusammenfassungFür Patienten mit hereditären Netzhautdystrophien (auf Englisch: inherited retinal dystrophies, IRD) ist die Früherkennung, Differenzialdiagnose und mögliche Therapieentscheidung von erheblicher persönlicher und sozialer Bedeutung. Für den Augenarzt kann dies aufgrund der Heterogenität der Erkrankungen und Verläufe sowie der Seltenheit der IRD eine Herausforderung sein. Die vorliegende Übersicht empfiehlt eine zielorientierte klinisch-ophthalmologische Diagnostik bei Verdacht auf IRD mit einer Bewertung der Relevanz der einzelnen Methoden und ihrer Kombination für Diagnose, Differenzialdiagnose und Beurteilung der Progression im Verlauf. Nach einer umfassenden Anamnese ist initial die Kombination von optischer Kohärenztomografie (OCT), Fundus- und Nahinfrarot-Autofluoreszenz zur Frühdiagnose einer IRD ggf. vor ophthalmoskopisch sichtbaren Läsionen sinnvoll. Spektrale Reflexionsfotografie, OCT-Angiografie und Fluorescence Lifetime Imaging Ophthalmoscopy sind bei einzelnen IRD hilfreich. Erlaubt die retinale Bildgebung keine sichere Diagnose, ist das multifokale Elektroretinogramm zur Frühdiagnose und das Ganzfeld-Elektroretinogramm zur Differenzialdiagnose von IRD geeignet. Eine Vorstellung in Schwerpunktzentren für IRD zur Differenzialdiagnostik und Therapie ist empfehlenswert.

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Prospective Cohort Study of Childhood-Onset Stargardt Disease: Fundus Autofluorescence Imaging, Progression, Comparison with Adult-Onset Disease, and Disease Symmetry

Cited by 47 publications

References 55 publications

Retinal Structure inRPE65-Associated Retinal Dystrophy

Retinal Structure inRPE65-Associated Retinal Dystrophy

Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease

Hereditäre Netzhautdystrophien: Kombination ophthalmologischer Methoden zur Optimierung des Readout

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