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Predicting pulmonary function testing from quantified computed tomography using machine learning algorithms in patients with COPD
Gawlitza, Joshua
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Sturm, Timo
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Spohrer, Kai
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Henzler, Thomas
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Akin, Ibrahim
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Schönberg, Stefan
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Borggrefe, Martin
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Haubenreisser, Holger
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Trinkmann, Frederik
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DOI:
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https://doi.org/10.3390/diagnostics9010033
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URL:
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https://madoc.bib.uni-mannheim.de/56213
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Weitere URL:
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https://www.mdpi.com/2075-4418/9/1/33/htm
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URN:
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urn:nbn:de:bsz:180-madoc-562131
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Dokumenttyp:
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Zeitschriftenartikel
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Erscheinungsjahr:
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2019
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Titel einer Zeitschrift oder einer Reihe:
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Diagnostics : Open Access Journal
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Band/Volume:
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9
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Heft/Issue:
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1
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Seitenbereich:
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1-33
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Ort der Veröffentlichung:
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Basel
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Verlag:
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MDPI
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ISSN:
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2075-4418
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Betriebswirtschaftslehre > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
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Bereits vorhandene Lizenz:
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 Creative Commons Namensnennung 4.0 International (CC BY 4.0)
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Fachgebiet:
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004 Informatik
610 Medizin, Gesundheit
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Freie Schlagwörter (Englisch):
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chronic obstructive pulmonary disease , machine learning , thorax
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Abstract:
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Introduction: Quantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests. Methods: 75 patients with diagnosed COPD underwent
body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration—expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction, k-nearest neighbours (kNN), gradient boosting, and multilayer perceptron. Results: The lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences
in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found. Conclusion: Different, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.
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Zusätzliche Informationen:
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Online-Ressource
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 Suche Autoren in
BASE:
Gawlitza, Joshua
;
Sturm, Timo
;
Spohrer, Kai
;
Henzler, Thomas
;
Akin, Ibrahim
;
Schönberg, Stefan
;
Borggrefe, Martin
;
Haubenreisser, Holger
;
Trinkmann, Frederik
Google Scholar:
Gawlitza, Joshua
;
Sturm, Timo
;
Spohrer, Kai
;
Henzler, Thomas
;
Akin, Ibrahim
;
Schönberg, Stefan
;
Borggrefe, Martin
;
Haubenreisser, Holger
;
Trinkmann, Frederik
ORCID:
Gawlitza, Joshua ; Sturm, Timo ; Spohrer, Kai  ORCID: 0000-0001-8659-7554 ; Henzler, Thomas ; Akin, Ibrahim ; Schönberg, Stefan ; Borggrefe, Martin ; Haubenreisser, Holger ; Trinkmann, Frederik
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