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@ARTICLE{Rttgers:1024402,
      author       = {Rüttgers, Mario and Waldmann, Moritz and Vogt, Klaus and
                      Ilgner, Justus and Schröder, Wolfgang and Lintermann,
                      Andreas},
      title        = {{A}utomated surgery planning for an obstructed nose by
                      combining computational fluid dynamics with reinforcement
                      learning},
      journal      = {Computers in biology and medicine},
      volume       = {173},
      issn         = {0010-4825},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-02145},
      pages        = {108383},
      year         = {2024},
      abstract     = {Septoplasty and turbinectomy are among the most common
                      interventions in the field of rhinology. Their constantly
                      debated success rates and the lack of quantitative flow data
                      of the entire nasal airway for planning the surgery
                      necessitate methodological improvement. Thus, physics-based
                      surgery planning is highly desirable. In this work, a novel
                      and accurate method is developed to enhance surgery planning
                      by physical aspects of respiration, i.e., to plan
                      anti-obstructive surgery, for the first time a reinforcement
                      learning algorithm is combined with large-scale
                      computational fluid dynamics simulations. The method is
                      integrated into an automated pipeline based on computed
                      tomography imaging. The proposed surgical intervention is
                      compared to a surgeon’s initial plan, or the maximum
                      possible intervention, which allows the quantitative
                      evaluation of the intended surgery. Two criteria are
                      considered: (i) the capability to supply the nasal airway
                      with air expressed by the pressure loss and (ii) the
                      capability to heat incoming air represented by the
                      temperature increase. For a test patient suffering from a
                      deviated septum near the nostrils and a bony spur further
                      downstream, the method recommends surgical interventions
                      exactly at these locations. For equal weights on the two
                      criteria (i) and (ii), the algorithm proposes a slightly
                      weaker correction of the deviated septum at the first
                      location, compared to the surgeon’s plan. At the second
                      location, the algorithm proposes to keep the bony spur. For
                      a larger weight on criterion (i), the algorithm tends to
                      widen the nasal passage by removing the bony spur. For a
                      larger weight on criterion (ii), the algorithm’s
                      suggestion approaches the pre-surgical state with narrowed
                      channels that favor heat transfer. A second patient is
                      investigated that suffers from enlarged turbinates in the
                      left nasal passage. For equal weights on the two criteria
                      (i) and (ii), the algorithm proposes a nearly complete
                      removal of the inferior turbinate, and a moderate reduction
                      of the middle turbinate. An increased weight on criterion
                      (i) leads to an additional reduction of the middle
                      turbinate, and a larger weight on criterion (ii) yields a
                      solution with only slight reductions of both turbinates,
                      i.e., focusing on a sufficient heat exchange between
                      incoming air and the air-nose interface. The proposed method
                      has the potential to improve the success rates of the
                      aforementioned surgeries and can be extended to further
                      biomedical flows.},
      cin          = {JSC},
      ddc          = {570},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733) /
                      HDS LEE - Helmholtz School for Data Science in Life, Earth
                      and Energy (HDS LEE) (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.compbiomed.2024.108383},
      url          = {https://juser.fz-juelich.de/record/1024402},
}