@article{KotliarHauserOrtneretal.2017,
  author    = {Kotliar, Konstantin and Hauser, Christine and Ortner, Marion and Muggenthaler, Claudia and Diehl-Schmid, Janine and Angermann, Susanne and Hapfelmeier, Alexander and Schmaderer, Christoph and Grimmer, Timo},
  title     = {Altered neurovascular coupling as measured by optical imaging: a biomarker for Alzheimer's disease},
  series = {Scientific Reports},
  volume    = {7},
  journal   = {Scientific Reports},
  number    = {1},
  publisher = {Springer Nature},
  address   = {Cham},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-017-13349-5},
  pages     = {1 -- 11},
  year      = {2017},
  language  = {en}
}
@article{WerfelGuenthnerHapfelmeieretal.2022,
  author    = {Werfel, Stanislas and G{\"u}nthner, Roman and Hapfelmeier, Alexander and Hanssen, Henner and Kotliar, Konstantin and Heemann, Uwe and Schmaderer, Christoph},
  title     = {Identification of cardiovascular high-risk groups from dynamic retinal vessel signals using untargeted machine learning},
  series = {Cardiovascular Research},
  volume    = {118},
  journal   = {Cardiovascular Research},
  number    = {2},
  editor    = {Guzik, Tomasz J.},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {0008-6363},
  doi       = {10.1093/cvr/cvab040},
  pages     = {612 -- 621},
  year      = {2022},
  abstract  = {Dynamic retinal vessel analysis (DVA) provides a non-invasive way to assess microvascular function in patients and potentially to improve predictions of individual cardiovascular (CV) risk. The aim of our study was to use untargeted machine learning on DVA in order to improve CV mortality prediction and identify corresponding response alterations.},
  language  = {en}
}