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Conference Paper: A critical discussion of computer analysis in medical imaging

TitleA critical discussion of computer analysis in medical imaging
Authors
KeywordsComposite scores
Computer analysis
Medical images
Issue Date2007
Citation
The 2007 International Conference of the American Thoracic Society (ATS), San Francisco, CA., 18-23 May 2007. In Proceedings of the American Thoracic Society, 2007, v. 4, n. 4, p. 347-349 How to Cite?
AbstractMedical imaging has increasingly provided surrogate endpoints in therapeutic trials. This use assumes that the interpretation of the images can be unbiased and reproducible and that the image attributes included in the interpretation are relevant to the mechanism of the trial. The principal motivation for computer analysis is to evaluate an attribute of the image as a metric in an algorithmic manner, independent of observer bias or variability. The metric is expected to reflect change in rough proportion with at least one aspect of the degree of disease or the effectiveness of the therapeutic intervention. If either condition is satisfied, the measure is quantitative. Visual interpretation explicitly or implicitly tends to be based on multiple image attributes. Explicit combination of multiple attributes yields composite scores. To evaluate the risk or probability of disease, they are useful. But the components of the scores can be combined only if they are mathematically isomorphic. For the evaluation of interventions, they are less useful because the effect on one component may be obscured by the lack of effect on other components. This article reviews quantification of air trapping in cystic fibrosis and quantification in general. Validation of any computer analysis can rely on agreement with visual interpreters (on average), they can be derived from first principles, or by agreement with an alternative method that measures the pathophysiological mechanism directly (xenon washout for air trapping). However, in the context of trials, the validation may come from a superior ability to detect objective change and to discriminate between affected and unaffected individuals.
Persistent Identifierhttp://hdl.handle.net/10722/198785
ISSN

 

DC FieldValueLanguage
dc.contributor.authorGoris, ML-
dc.contributor.authorZhu, H-
dc.contributor.authorRobinson, TE-
dc.date.accessioned2014-07-09T03:42:15Z-
dc.date.available2014-07-09T03:42:15Z-
dc.date.issued2007-
dc.identifier.citationThe 2007 International Conference of the American Thoracic Society (ATS), San Francisco, CA., 18-23 May 2007. In Proceedings of the American Thoracic Society, 2007, v. 4, n. 4, p. 347-349-
dc.identifier.issn1546-3222-
dc.identifier.urihttp://hdl.handle.net/10722/198785-
dc.description.abstractMedical imaging has increasingly provided surrogate endpoints in therapeutic trials. This use assumes that the interpretation of the images can be unbiased and reproducible and that the image attributes included in the interpretation are relevant to the mechanism of the trial. The principal motivation for computer analysis is to evaluate an attribute of the image as a metric in an algorithmic manner, independent of observer bias or variability. The metric is expected to reflect change in rough proportion with at least one aspect of the degree of disease or the effectiveness of the therapeutic intervention. If either condition is satisfied, the measure is quantitative. Visual interpretation explicitly or implicitly tends to be based on multiple image attributes. Explicit combination of multiple attributes yields composite scores. To evaluate the risk or probability of disease, they are useful. But the components of the scores can be combined only if they are mathematically isomorphic. For the evaluation of interventions, they are less useful because the effect on one component may be obscured by the lack of effect on other components. This article reviews quantification of air trapping in cystic fibrosis and quantification in general. Validation of any computer analysis can rely on agreement with visual interpreters (on average), they can be derived from first principles, or by agreement with an alternative method that measures the pathophysiological mechanism directly (xenon washout for air trapping). However, in the context of trials, the validation may come from a superior ability to detect objective change and to discriminate between affected and unaffected individuals.-
dc.languageeng-
dc.relation.ispartofProceedings of the American Thoracic Society-
dc.subjectComposite scores-
dc.subjectComputer analysis-
dc.subjectMedical images-
dc.titleA critical discussion of computer analysis in medical imaging-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1513/pats.200701-014HT-
dc.identifier.pmid17652499-
dc.identifier.scopuseid_2-s2.0-34548205843-
dc.identifier.volume4-
dc.identifier.issue4-
dc.identifier.spage347-
dc.identifier.epage349-

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