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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},
}