Cargando…
Machine Learning to Predict Faricimab Treatment Outcome in Neovascular Age-Related Macular Degeneration
PURPOSE: To develop machine learning (ML) models to predict, at baseline, treatment outcomes at month 9 in patients with neovascular age-related macular degeneration (nAMD) receiving faricimab. DESIGN: Retrospective proof of concept study. PARTICIPANTS: Patients enrolled in the phase II AVENUE trial...
Autores principales: | Kikuchi, Yusuke, Kawczynski, Michael G., Anegondi, Neha, Neubert, Ales, Dai, Jian, Ferrara, Daniela, Quezada-Ruiz, Carlos |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585644/ https://www.ncbi.nlm.nih.gov/pubmed/37868796 http://dx.doi.org/10.1016/j.xops.2023.100385 |
Ejemplares similares
-
Faricimab for the Treatment of Diabetic Macular Edema and Neovascular Age-Related Macular Degeneration
por: Ferro Desideri, Lorenzo, et al.
Publicado: (2023) -
Intravitreal Faricimab for Aflibercept-Resistant Neovascular Age-Related Macular Degeneration
por: Rush, Ryan B, et al.
Publicado: (2022) -
Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration
por: Maunz, Andreas, et al.
Publicado: (2023) -
TENAYA and LUCERNE: Rationale and Design for the Phase 3 Clinical Trials of Faricimab for Neovascular Age-Related Macular Degeneration
por: Khanani, Arshad M., et al.
Publicado: (2021) -
Initial Real-World Experience with Faricimab in Treatment-Resistant Neovascular Age-Related Macular Degeneration
por: Leung, Ella H, et al.
Publicado: (2023)