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@ARTICLE{Ayala:274382,
      author       = {L. Ayala$^*$ and T. Adler$^*$ and S. Seidlitz$^*$ and S.
                      Wirkert$^*$ and C. Engels and A. Seitel$^*$ and J.
                      Sellner$^*$ and A. Aksenov and M. Bodenbach and P. Bader and
                      S. Baron and A. Vemuri$^*$ and M. Wiesenfarth$^*$ and N.
                      Schreck$^*$ and D. Mindroc$^*$ and M. Tizabi$^*$ and S.
                      Pirmann$^*$ and B. Everitt$^*$ and A. Kopp-Schneider$^*$ and
                      D. Teber and L. Maier-Hein$^*$},
      title        = {{S}pectral imaging enables contrast agent-free real-time
                      ischemia monitoring in laparoscopic surgery.},
      journal      = {Science advances},
      volume       = {9},
      number       = {10},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {DKFZ-2023-00582},
      pages        = {eadd6778},
      year         = {2023},
      note         = {#EA:E130#LA:E130#},
      abstract     = {Laparoscopic surgery has evolved as a key technique for
                      cancer diagnosis and therapy. While characterization of the
                      tissue perfusion is crucial in various procedures, such as
                      partial nephrectomy, doing so by means of visual inspection
                      remains highly challenging. We developed a laparoscopic
                      real-time multispectral imaging system featuring a compact
                      and lightweight multispectral camera and the possibility to
                      complement the conventional surgical view of the patient
                      with functional information at a video rate of 25 Hz. To
                      enable contrast agent-free ischemia monitoring during
                      laparoscopic partial nephrectomy, we phrase the problem of
                      ischemia detection as an out-of-distribution detection
                      problem that does not rely on data from any other patient
                      and uses an ensemble of invertible neural networks at its
                      core. An in-human trial demonstrates the feasibility of our
                      approach and highlights the potential of spectral imaging
                      combined with advanced deep learning-based analysis tools
                      for fast, efficient, reliable, and safe functional
                      laparoscopic imaging.},
      keywords     = {Humans / Contrast Media / Nephrectomy: methods / Neural
                      Networks, Computer / Laparoscopy: methods / Ischemia /
                      Contrast Media (NLM Chemicals)},
      cin          = {E130 / C060},
      ddc          = {500},
      cid          = {I:(DE-He78)E130-20160331 / I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36897951},
      pmc          = {pmc:PMC10005169},
      doi          = {DOI: 10.1126/sciadv.add6778},
      url          = {https://inrepo02.dkfz.de/record/274382},
}