| Autor: |
Andreas Maunz, PhD, Laura Barras, MSc, Michael G. Kawczynski, MS, Jian Dai, PhD, Aaron Y. Lee, MD, MSc, Richard F. Spaide, MD, Jayashree Sahni, MBBS, MD, Daniela Ferrara, MD, PhD |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Ophthalmology Science, Vol 3, Iss 4, Pp 100319- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-9145 |
| DOI: |
10.1016/j.xops.2023.100319 |
| Popis: |
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 model of baseline age and BCVA. Quantitative OCT features were derived by a DL segmentation model on the volume images, including retinal layer volumes and thicknesses, and retinal fluid biomarkers, including statistics on fluid volume and distribution. Main Outcome Measures: Prognostic ability of the models was evaluated using coefficient of determination (R2) and median absolute error (MAE; letters). Results: In the first cross-validation split, mean R2 (MAE) of the Lasso min, Lasso 1SE, CatBoost, and RF models was 0.46 (7.87), 0.42 (8.43), 0.45 (7.75), and 0.43 (7.60), respectively. These models ranked higher than or similar to the benchmark model (mean R2, 0.41; mean MAE, 8.20 letters) and better than OCT-only models (mean R2: Lasso OCT min, 0.20; Lasso OCT 1SE, 0.16; DL, 0.34). The Lasso min model was selected for detailed analysis; mean R2 (MAE) of the Lasso min and benchmark models for 1000 repeated cross-validation splits were 0.46 (7.7) and 0.42 (8.0), respectively. Conclusions: Machine learning models based on AI-segmented OCT features and clinical variables at baseline may predict future response to ranibizumab treatment in patients with nAMD. However, further developments will be needed to realize the clinical utility of such AI-based tools. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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