Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning

Computers in Biology and Medicine, vol. 173, pp. 108383

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.

Authors 6

1. Mario Rüttgers corresponding Aachen Institute of Aerodynamics Chair of Fluid Mechanics Center for Simulation and Data Science

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany; Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany; Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany. Electronic address: m.ruettgers@fz-juelich.de

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany

   Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

2. Moritz Waldmann Aachen Institute of Aerodynamics Chair of Fluid Mechanics Center for Simulation and Data Science

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany; Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany

   Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

3. Klaus Vogt

   University of Latvia

   Affiliation as printed

   Faculty of Medicine, Center of Experimental Surgery, University of Latvia, 1586 Riga, Latvia

4. Justus F. R. Ilgner Aachen Department of Otorhinolaryngology—Head and Neck Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Otorhinolaryngology, Head and Neck Surgery, Uniklinik RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany

5. Wolfgang Schröder Aachen Institute of Aerodynamics Chair of Fluid Mechanics Center for Simulation and Data Science

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany; Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

   Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Wüllnerstraße 5a, 52062 Aachen, Germany

   Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

6. Andreas Lintermann

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany; Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

   Jülich Aachen Research Alliance, Center for Simulation and Data Science, 52074 Aachen, Germany

   Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52425 Jülich, Germany