ArticleMedical & biological engineering & computing2022
An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Understanding the mismatch between in-vivo and in-silico rhinomanometry.Medical & biological engineering & computing · 2026Article
- A Nasal Resistance Measurement System Based on Multi-Sensor Fusion of Pressure and Flow.Micromachines · 2025Article
- Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2024Review
- A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023Article
- Comparison of rhinomanometric and computational fluid dynamic assessment of nasal resistance with respect to measurement accuracy.International journal of computer assisted radiology and surgery · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a Jupyter environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation. Graphical Abstract Workflow for enhanced diagnostics in rhinology.
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Registered trials
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