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Can I read Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration on EtoBox?
Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration by Andreas Maunz; Laura Barras; Michael G. Kawczynski; Jian Dai; Aaron Y. Lee; Richard F. Spaide; Jayashree Sahni; Daniela Ferrara is a Medicine article available to read on EtoBox.
What is Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration about?
## Purpose Neovascular age-related macular degeneration (nAMD) shows variable treatment response to intravitreal anti-VEGF. This analysis compared the potential of different artificial intelligence (AI)-based machine learning models using OCT and clinical variables to accurately predict at baseline the best-corrected visual acuity (BCVA) at 9 months in response to ranibizumab in patients with nAMD. ## Design Retrospective analysis. ## Participants Baseline and imaging data from patients with subfoveal choroidal neovascularization secondary to age-related macular dengeration. ## Methods Baseline data from 502 study eyes from the HARBOR (NCT00891735) prospective clinical trial (monthly ranibizumab 0.5 and 2.0 mg arms) were pooled; 432 baseline OCT volume scans were included in the analysis. Seven models, based on baseline quantitative OCT features (Least absolute shrinkage and selection operator [Lasso] OCT minimum [min], Lasso OCT 1 standard error [SE]); on quantitative OCT features and clinical variables at baseline (Lasso min, Lasso 1SE, CatBoost, RF [random forest]); or on baseline OCT images only (deep learning [DL] model), were systematically compared with a benchmark linear mo
Who reads Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Andreas Maunz; Laura Barras; Michael G. Kawczynski; Jian Dai; Aaron Y. Lee; Richard F. Spaide; Jayashree Sahni; Daniela Ferrara
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Medicine (Health Sciences)