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Home Ophthalmology Geographic Atrophy

Geographic Atrophy

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Personalised Healthcare
May 6 / Roche and Genentech
Predicting Geographic Atrophy Growth Rate With Clinical and Derived Imaging Features
Geographic atrophy progression prediction models have many use cases in clinical trials, including covariate adjustment. In the future, these models may also be useful for patient counseling and treatment decisions in clinical practice. End-to-end deep learning (DL) models with fundus autofluorescence (FAF) baseline images have outperformed feature-based models trained on standard clinical and imaging features, including those with run-in data. However, the imaging features that drive the success of these DL models remain unknown. This poster described the development of imaging-feature-based models that aim to achieve similar performance to FAF-DL models to help derive prognostic imaging biomarkers and create more interpretable models.
Ophthalmology Geographic Atrophy Machine Learning ARVO-2024

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Personalised Healthcare
May 6 / Roche and Genentech
Generalizability of foundation models for geographic atrophy (GA) lesion segmentation in fundus autofluorescence (FAF)
High-capacity, transformer-based, deep-learning models trained in a self-supervised manner on large datasets are referred to as foundation models (FMs). These models may offer generalizability to unseen domains/tasks suggesting the potential to accelerate algorithm development, particularly with limited data. This poster explores the generalizability of vision foundation models (FMs), DINOv21 and SAM2, to an unseen ophthalmology task, segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF), and characterizes performance as a function of dataset size.
Ophthalmology Geographic Atrophy Machine Learning ARVO-2024

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Oct 9 / Roche and Genentech
Visual Loss in Geographic Atrophy: A Comprehensive Exploratory Analysis of Function and Morphology From the Lampalizumab Trial Programme
Geographic atrophy (GA) is an advanced form of age-related macular degeneration and a major cause of vision impairment worldwide. There is large variability in GA growth rate between individuals and is highly influenced by morphological factors. Interventional clinical trials have shown reductions in GA growth as measured on fundus autofluorescence images with no corresponding reduction in any other clinical measure of visual function such as Best Corrected Visual Acuity (BCVA). However, there is major need for analyses of change in function by morphological characteristics and by growth rate. The aim of this study is to explore how baseline factors and GA expansion rates influence the rate of BCVA loss.
Ophthalmology Geographic Atrophy EURETINA-2023
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This website is intended for healthcare professionals outside the United Kingdom (UK) and Australia. Registration status and prescribing information of medicinal products may differ between countries. Please refer to local product information for any medicinal products mentioned on this website.