AIMC Topic: Fluorescein Angiography

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Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment.

The British journal of ophthalmology
OBJECTIVE: To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on O...

Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis.

Ocular immunology and inflammation
PURPOSE: Retinal vasculitis (RV) is characterised by retinal vascular leakage, occlusion or both on fluorescein angiography (FA). There is no standard scheme available to segment RV features. We aimed to develop a deep learning model to segment both ...

Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical set...

Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration.

Acta ophthalmologica
PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.

Quality assessment of colour fundus and fluorescein angiography images using deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Image quality assessment (IQA) is crucial for both reading centres in clinical studies and routine practice, as only adequate quality allows clinicians to correctly identify diseases and treat patients accordingly. Here we aim to dev...

Automated segmentation of ultra-widefield fluorescein angiography of diabetic retinopathy using deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Retinal capillary non-perfusion (NP) and neovascularisation (NV) are two of the most important angiographic changes in diabetic retinopathy (DR). This study investigated the feasibility of using deep learning (DL) models to automatic...

Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven a...

Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy.

Scientific reports
To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patie...

Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology.

Journal of biophotonics
PURPOSE: The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular mainly involves degeneration and impaired function of the macular region. Reliable optic disc and m...