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dc.contributor.authorRecenti, Marco
dc.contributor.authorRicciardi, Carlo
dc.contributor.authorEdmunds, Kyle
dc.contributor.authorGislason, Magnus K
dc.contributor.authorGargiulo, Paolo
dc.date.accessioned2020-10-19T14:49:20Z
dc.date.available2020-10-19T14:49:20Z
dc.date.issued2020-04-01
dc.date.submitted2020-10
dc.identifier.citationRecenti M, Ricciardi C, Edmunds K, Gislason MK, Gargiulo P. Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images. Eur J Transl Myol. 2020 Apr 1;30(1):8892. doi: 10.4081/ejtm.2019.8892. PMID: 32499893; PMCID: PMC7254455.en_US
dc.identifier.issn2037-7452
dc.identifier.pmid32499893
dc.identifier.doi10.4081/ejtm.2019.8892
dc.identifier.urihttp://hdl.handle.net/2336/621543
dc.descriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Downloaden_US
dc.description.abstractThe nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT images distributions was developed for the quantitative characterization of soft tissue changes according to the lower extremity function of elderly subjects. In this regard, the NTRA method defines 11 subject-specific soft tissue parameters and has illustrated high sensitivity to changes in skeletal muscle form and function. The present work further explores the use of these 11 NTRA parameters in the construction of a machine learning (ML) system to predict body mass index and isometric leg strength using tree-based regression algorithms. Results obtained from these models demonstrate that when using an ML approach, these soft tissue features have a significant predictive value for these physiological parameters. These results further support the use of NTRA-based ML predictive assessment and support the future investigation of other physiological parameters and comorbidities. Keywords: Computed Tomography; Machine learning; body mass index; isometric leg strength; soft tissue.en_US
dc.language.isoenen_US
dc.publisherPAGEPRESS PUBLen_US
dc.relation.urlhttps://www.pagepressjournals.org/index.php/bam/article/view/8892en_US
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254455/en_US
dc.subjectComputed Tomographyen_US
dc.subjectMachine learningen_US
dc.subjectbody mass indexen_US
dc.subjectisometric leg strengthen_US
dc.subjectsoft tissueen_US
dc.titleMachine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.en_US
dc.typeArticleen_US
dc.contributor.department1Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland. 2Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy. 3Department of Science, Landspítali, Reykjavík, Iceland.en_US
dc.identifier.journalEuropean journal of translational myologyen_US
dc.rights.accessOpen Access - Opinn aðganguren_US
dc.departmentcodeRES12
dc.source.journaltitleEuropean journal of translational myology
dc.source.volume30
dc.source.issue1
dc.source.beginpage8892
dc.source.endpage
dc.source.countryItaly


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