Show simple item record

dc.contributor.authorRicciardi, Carlo
dc.contributor.authorJónsson, Halldór
dc.contributor.authorJacob, Deborah
dc.contributor.authorImprota, Giovanni
dc.contributor.authorRecenti, Marco
dc.contributor.authorGíslason, Magnús Kjartan
dc.contributor.authorCesarelli, Giuseppe
dc.contributor.authorEsposito, Luca
dc.contributor.authorMinutolo, Vincenzo
dc.contributor.authorBifulco, Paolo
dc.contributor.authorGargiulo, Paolo
dc.date.accessioned2020-12-14T21:51:37Z
dc.date.available2020-12-14T21:51:37Z
dc.date.issued2020-10-14
dc.date.submitted2020-12
dc.identifier.citationRicciardi C, Jónsson H, Jr., Jacob D, Improta G, Recenti M, Gíslason MK, et al. Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty. Diagnostics (Basel, Switzerland). 2020;10(10): 815. doi:10.3390/diagnostics10100815.en_US
dc.identifier.issn2075-4418
dc.identifier.pmid33066350
dc.identifier.doi10.3390/diagnostics10100815
dc.identifier.urihttp://hdl.handle.net/2336/621602
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.abstractThere are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients. Keywords: clinical decision making; database analyses; electromyography; machine learning; total hip arthroplasty.en_US
dc.description.sponsorshipUniversity of Reykjavik Icelandic National Hospital Rannis (Rannis Icelandic Research Fund (Rannsoknasjodur)) A&C M-C Foundation of Translational Myology, Padova, Italyen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urlhttps://www.mdpi.com/2075-4418/10/10/815en_US
dc.subjectclinical decision makingen_US
dc.subjectdatabase analysesen_US
dc.subjectelectromyographyen_US
dc.subjectmachine learningen_US
dc.subjecttotal hip arthroplastyen_US
dc.subjectMjaðmaaðgerðiren_US
dc.subjectLiðskiptaaðgerðiren_US
dc.subject.meshArthroplasty, Replacement, Hipen_US
dc.subject.meshClinical Decision-Makingen_US
dc.titleImproving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty.en_US
dc.typeArticleen_US
dc.contributor.department1Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', 80131 Naples, Italy. 2Institute for Biomedical and Neural Engineering, Reykjavík University, 102 Reykjavík, Iceland. 3Faculty of Medicine, University of Iceland, 102 Reykjavík, Iceland. 4Landspítali Hospital, Orthopaedic Clinic, 102 Reykjavík, Iceland. 5Department of Public Health, University Hospital of Naples 'Federico II', 80125 Naples, Italy. 6Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", 80125 Naples, Italy. 7Istituto Italiano di Tecnologia, 80125 Naples, Italy. 8Department Engineering, University of Campania Luigi Vanvitelli, 81100 Aversa (CE), Italy. 9Department of Electrical Engineering and Information Technologies, University Hospital of Naples 'Federico II', 80125 Naples, Italy. 10Department of Science, Landspítali Hospital, 102 Reykjavík, Iceland.en_US
dc.identifier.journalDiagnostics (Basel, Switzerland)en_US
dc.rights.accessOpen Access - Opinn aðganguren_US
dc.departmentcodeORT12
dc.departmentcodeRES12
dc.source.journaltitleDiagnostics (Basel, Switzerland)
dc.source.volume10
dc.source.issue10
refterms.dateFOA2020-12-14T21:51:38Z
dc.source.countrySwitzerland


Files in this item

Thumbnail
Name:
Improving ....pdf
Size:
1.679Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record