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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
;
Akin, Ibrahim
;
Schönberg, Stefan
;
Borggrefe, Martin
;
Haubenreisser, Holger
;
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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Additional 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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Document Type:
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Article
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Year of publication:
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2019
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The title of a journal, publication series:
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Diagnostics : Open Access Journal
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Volume:
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9
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Issue number:
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1
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Page range:
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1-33
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Place of publication:
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Basel
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Publishing house:
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MDPI
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ISSN:
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2075-4418
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Publication language:
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English
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Institution:
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Business School > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
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Pre-existing license:
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 Creative Commons Attribution 4.0 International (CC BY 4.0)
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Subject:
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004 Computer science, internet
610 Medicine and health
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Keywords (English):
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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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Additional information:
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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. |
 Search Authors 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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