An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology
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 m...
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| Veröffentlicht in: | Medical & biological engineering & computing 2022-02, Vol.60 (2), p.365-391 |
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| creator | Waldmann, Moritz Grosch, Alice Witzler, Christian Lehner, Matthias Benda, Odo Koch, Walter Vogt, Klaus Kohn, Christopher Schröder, Wolfgang Göbbert, Jens Henrik Lintermann, Andreas |
| description | 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. |
| doi_str_mv | 10.1007/s11517-021-02446-3 |
| format | Article |
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Graphical Abstract
Workflow for enhanced diagnostics in rhinology.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-021-02446-3</identifier><identifier>PMID: 34950998</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Computer Applications ; Computer Simulation ; Data processing ; Human Physiology ; Imaging ; Learning algorithms ; Machine Learning ; Medical imaging ; Mucosa ; Original ; Original Article ; Pipelining (computers) ; Radiology ; Simulation ; Software ; Workflow</subject><ispartof>Medical & biological engineering & computing, 2022-02, Vol.60 (2), p.365-391</ispartof><rights>The Author(s) 2021</rights><rights>2021. The Author(s).</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-79056a3039ce94f07af811cbbbf1428110e4c065093ffd960f86f558084334353</citedby><cites>FETCH-LOGICAL-c474t-79056a3039ce94f07af811cbbbf1428110e4c065093ffd960f86f558084334353</cites><orcidid>0000-0001-7895-761X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-021-02446-3$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-021-02446-3$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,315,782,786,887,27933,27934,41497,42566,51328</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34950998$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Waldmann, Moritz</creatorcontrib><creatorcontrib>Grosch, Alice</creatorcontrib><creatorcontrib>Witzler, Christian</creatorcontrib><creatorcontrib>Lehner, Matthias</creatorcontrib><creatorcontrib>Benda, Odo</creatorcontrib><creatorcontrib>Koch, Walter</creatorcontrib><creatorcontrib>Vogt, Klaus</creatorcontrib><creatorcontrib>Kohn, Christopher</creatorcontrib><creatorcontrib>Schröder, Wolfgang</creatorcontrib><creatorcontrib>Göbbert, Jens Henrik</creatorcontrib><creatorcontrib>Lintermann, Andreas</creatorcontrib><title>An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>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.</description><subject>Algorithms</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Computer Applications</subject><subject>Computer Simulation</subject><subject>Data processing</subject><subject>Human Physiology</subject><subject>Imaging</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Medical imaging</subject><subject>Mucosa</subject><subject>Original</subject><subject>Original Article</subject><subject>Pipelining 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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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34950998</pmid><doi>10.1007/s11517-021-02446-3</doi><tpages>27</tpages><orcidid>https://orcid.org/0000-0001-7895-761X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Medical & biological engineering & computing, 2022-02, Vol.60 (2), p.365-391 |
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| subjects | Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Computer Applications Computer Simulation Data processing Human Physiology Imaging Learning algorithms Machine Learning Medical imaging Mucosa Original Original Article Pipelining (computers) Radiology Simulation Software Workflow |
| title | An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology |
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