Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.

@article{Quellec2019FeasibilityOS,
  title={Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.},
  author={Gw{\'e}nol{\'e} Quellec and Jens Kowal and Pascal Willy Hasler and Hendrik P. N. Scholl and Sandrine A. Zweifel and Balaskas Konstantinos and Jo{\~a}o Emanuel Ramos de Carvalho and Tjebo F. C. Heeren and Catherine A. Egan and Adnan Tufail and Peter M Maloca},
  journal={Acta ophthalmologica},
  year={2019}
}
PURPOSE A retrospective pilot study is conducted to demonstrate the utility of a novel support vector machine learning (SVML) algorithm in a small three-dimensional (3D) sample yielding sparse optical coherence tomography (spOCT) data for the automatic monitoring of neovascular (wet) age-related macular degeneration (wAMD). METHODS From the anti-vascular endothelial growth factor injection database, 588 consecutive pairs of OCT volumes (57.624 B-scans) were selected in 70 randomly chosen wAMD… CONTINUE READING
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