AIMC Topic: Wet Macular Degeneration

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Updates on novel and traditional OCT and OCTA biomarkers in nAMD.

Eye (London, England)
Predictivity of optical coherence tomography (OCT) examination for the development of neovascular age-related macular degeneration (nAMD) was demonstrated to be superior compared to other methods, suggesting it as an elective method for screening pur...

Artificial intelligence-based analysis of retinal fluid volume dynamics in neovascular age-related macular degeneration and association with vision and atrophy.

Eye (London, England)
BACKGROUND/OBJECTIVES: To characterise morphological changes in neovascular age-related macular degeneration (nAMD) during anti-angiogenic therapy and explore relationships with best-corrected visual acuity (BCVA) and development of macular atrophy (...

Linking disease activity with optical coherence tomography angiography in neovascular age related macular degeneration using artificial intelligence.

Scientific reports
To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OC...

Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine.

The British journal of ophthalmology
AIM: To predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real...

Evaluation of choroid vascular layer thickness in wet age-related macular degeneration using artificial intelligence.

Photodiagnosis and photodynamic therapy
PURPOSE: To facilitate the assessment of choroid vascular layer thickness in patients with wet age-related macular degeneration (AMD) using artificial intelligence (AI).

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.

Preliminary analysis of predicting the first recurrence in patients with neovascular age-related macular degeneration using deep learning.

BMC ophthalmology
BACKGROUND: To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF).