Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration.

Maunz, Andreas; Barras, Laura; Kawczynski, Michael G; Dai, Jian; Lee, Aaron Y; Spaide, Richard F; Sahni, Jayashree; Ferrara, Daniela

Maunz, Andreas; Barras, Laura; Kawczynski, Michael G; Dai, Jian; Lee, Aaron Y; Spaide, Richard F; Sahni, Jayashree; Ferrara, Daniela.

Afiliación

Maunz A; Roche Pharma Research and Early Development, F. Hoffmann-La Roche AG, Basel, Switzerland.
Barras L; Roche Data & Statistical Sciences, Genentech, Inc, South San Francisco, California.
Kawczynski MG; Roche Personalized Healthcare Program, Genentech, Inc, South San Francisco, California.
Dai J; Roche Personalized Healthcare Program, Genentech, Inc, South San Francisco, California.
Lee AY; Department of Ophthalmology, School of Medicine, University of Washington, Seattle, Washington.
Spaide RF; Vitreous Retina Macula Consultants of New York, New York, New York.
Sahni J; Roche Pharma Research and Early Development, F. Hoffmann-La Roche AG, Basel, Switzerland.
Ferrara D; Roche Personalized Healthcare Program, Genentech, Inc, South San Francisco, California.

Ophthalmol Sci ; 3(4): 100319, 2023 Dec.

Article en En | MEDLINE | ID: mdl-37304043

Palabras clave

Machine learning; Neovascular age-related macular degenration; Optical coherence tomography; Prediction; Ranibizumab

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ophthalmol Sci Año: 2023 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Países Bajos

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