Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema.

Journal: Ophthalmology. Retina
PMID:

Abstract

PURPOSE: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related macular degeneration (nAMD), retinal vein occlusion (RVO), and diabetic macular edema (DME) treated according to a treat-and-extend regimen (TER).

Authors

  • Mathias Gallardo
    AIMI, ARTORG Center, University of Bern, Bern, Switzerland. Electronic address: Mathias.Gallardo@gmail.com.
  • Marion R Munk
    Department of Ophthalmology, Inselspital-Bern University Hospital, University of Bern, Bern, Switzerland.
  • Thomas Kurmann
  • Sandro De Zanet
    RetinAI Medical AG, Bern, Switzerland.
  • Agata Mosinska
    Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, Station 15, Lausanne CH-1015, Switzerland.
  • Isıl Kutlutürk Karagoz
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland.
  • Martin S Zinkernagel
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland.
  • Sebastian Wolf
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland.
  • Raphael Sznitman
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.