Projects per year
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.
Original language | English |
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Article number | 16491 |
Journal | Scientific Reports |
Volume | 10 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2020 |
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Accelerating the identification and treatment of splice-altering mutations underlying inherited retinal diseases
Chen, F., Fletcher, S., McLenachan, S. & Cunningham, P.
National Health & Medical Research Council NHMRC
1/01/20 → 31/12/24
Project: Research
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MRFF - Developing Personalised Treatment for Retinal Degeneration
National Health & Medical Research Council NHMRC
1/01/18 → 31/12/21
Project: Research
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From discovery to therapy in genetic eye diseases
Mackey, D., Craig, J., Hewitt, A., Burdon, K., Jamieson, R., Grigg, J., MacGregor, S., Chen, F., Otlowski, M. & Schofield, D.
National Health & Medical Research Council NHMRC
1/01/16 → 31/12/20
Project: Research