Bringing Personalized Medicine to Rhinology
Using Computational Fluid Dynamics and Artificial Intelligence to Understand Pathologies and to Perform Surgery Planning
The nasal cavity is one of the most important organs of the human body. Its various functionalities are essential for the well-being of the individual person. It is responsible for the sense of smell, supports degustation, and filters, tempers, and moistens the inhaled air to provide optimal conditions for the lung. Diseases of the nasal cavity like chronic rhinosinusitis, septal deviation, or nasal polyps may lead to restrictions or complete loss of these functionalities [1, 2]. A decreased respiratory capability, the development of irritations and inflammations, and lung diseases can be the consequences.
The shape of the nasal cavity varies from person to person with stronger changes being present in pathological cases. A decent analysis on a per-patient basis is hence crucial to plan for a surgery with a successful outcome. Nowadays diagnostic methods rely on morphological analyses of the shape of the nasal cavity. They employ methods of medical imaging such as computed tomography (CT) or magnetic resonance imaging (MRI), and nasal endoscopy . Such methods, however, do not cover the fluid mechanics of respiration, which are essential to understand the impact of a pathology on the quality of respiration, and to plan for a surgery. Only a meaningful and physics-based diagnosis can help to adequately understand the functional efficiency of the nasal cavity, to quantify the impact of different pathologies on respiration, and to support surgeons in decision making.
Figure 1: Visualization of the flow in the human nasal cavity. The streamlines are colored by the velocity magnitude |v|. The inset on the right shows a magnification highlighting the boundary-refined computational mesh.
Novel physics-based methods to diagnose pathologies in the human respiratory systemhave recently evolved to include results of computational fluid dynamics (CFD) simulations (an example is shown in Fig. 1). CFD methods allow to numerically quantify the functions of the nasal cavity by analyzing fluid mechanical properties of respiratory flows, e.g., the pressure loss, the temperature distribution, or the mass flux distribution . Of higher interest than solely estimating a patient’s current condition is a non-invasive method to analyze planned structural changes, or in other words, a surgery plan . That is, there is a need to develop a chain of tools that form a data generation, processing, and analysis pipeline.
To be included in clinical diagnoses, realistic results for individual patients should be available in a short amount of time. This can be achieved by accelerating various components of such a pipeline by using high-performance computing (HPC) systems and artificial intelligence (AI) methods for pipeline automation, and fast inference for flow predictions. This means, such a pipeline needs to intertwine medical data/knowledge and their preparation for further processing using AI, AI-driven CFD simulations running on HPC systems, and methods leading to meaningful results to support surgery planning.
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