Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT

Schmidt-Erfurth, Ursula; Mai, Julia; Reiter, Gregor S.; Riedl, Sophie; Vogl, Wolf-Dieter; Sadeghipour, Amir; McKeown, Alex; Foos, Emma; Scheibler, Lukas; Bogunovic, Hrvoje

doi:10.1016/j.ophtha.2024.08.017

Ophthalmology

2024

DOI: 10.1016/j.ophtha.2024.08.017

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Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT

Ursula Schmidt-Erfurth,

Julia Mai,

Gregor S. Reiter

et al.

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2024

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Cited by 3 publications

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A Novel Management Challenge in Age-Related Macular Degeneration

Reiter,

Lachinov,

Bühl

et al. 2024

Ophthalmology Retina

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A Novel Management Challenge in Age-Related Macular Degeneration

Reiter,

Lachinov,

Bühl

et al. 2024

Ophthalmology Retina

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AI in the clinical management of GA: A novel therapeutic universe requires novel tools

Reiter,

Mai,

Riedl

et al. 2024

Progress in Retinal and Eye Research

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Artificial intelligence for geographic atrophy: pearls and pitfalls

Enzendorfer,

Schmidt-Erfurth

2024

Current Opinion in Ophthalmology

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Purpose of review This review aims to address the recent advances of artificial intelligence (AI) in the context of clinical management of geographic atrophy (GA), a vision-impairing late-stage manifestation of age-related macular degeneration (AMD). Recent findings Recent literature shows substantial advancements in the development of AI systems to segment GA lesions on multimodal retinal images, including color fundus photography (CFP), fundus autofluorescence (FAF) and optical coherence tomography (OCT), providing innovative solutions to screening and early diagnosis. Especially, the high resolution and 3D-nature of OCT has provided an optimal source of data for the training and validation of novel algorithms. The use of AI to measure progression in the context of newly approved GA therapies, has shown that AI methods may soon be indispensable for patient management. To date, while many AI models have been reported on, their implementation in the real-world has only just started. The aim is to make the benefits of AI-based personalized treatment accessible and far-reaching. Summary The most recent advances (pearls) and challenges (pitfalls) associated with AI methods and their clinical implementation in the context of GA will be discussed.

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Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT

Cited by 3 publications

References 42 publications

A Novel Management Challenge in Age-Related Macular Degeneration

A Novel Management Challenge in Age-Related Macular Degeneration

AI in the clinical management of GA: A novel therapeutic universe requires novel tools

Artificial intelligence for geographic atrophy: pearls and pitfalls

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