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Open AccessArticle

Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty

1
Department of Advanced Biomedical Sciences, University Hospital of Naples ‘Federico II’, 80131 Naples, Italy
2
Institute for Biomedical and Neural Engineering, Reykjavík University, 102 Reykjavík, Iceland
3
Faculty of Medicine, University of Iceland, 102 Reykjavík, Iceland
4
Landspítali Hospital, Orthopaedic Clinic, 102 Reykjavík, Iceland
5
Department of Public Health, University Hospital of Naples ‘Federico II’, 80125 Naples, Italy
6
Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, 80125 Naples, Italy
7
Istituto Italiano di Tecnologia, 80125 Naples, Italy
8
Department Engineering, University of Campania Luigi Vanvitelli, 81100 Aversa (CE), Italy
9
Department of Electrical Engineering and Information Technologies, University Hospital of Naples ‘Federico II’, 80125 Naples, Italy
10
Department of Science, Landspítali Hospital, 102 Reykjavík, Iceland
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(10), 815; https://doi.org/10.3390/diagnostics10100815
Received: 10 September 2020 / Revised: 8 October 2020 / Accepted: 12 October 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Skeletal Muscle Diagnostics and Managements)
There 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. View Full-Text
Keywords: database analyses; electromyography; machine learning; clinical decision making; total hip arthroplasty database analyses; electromyography; machine learning; clinical decision making; total hip arthroplasty
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MDPI and ACS Style

Ricciardi, C.; Jónsson, H., Jr.; Jacob, D.; Improta, G.; Recenti, M.; Gíslason, M.K.; Cesarelli, G.; Esposito, L.; Minutolo, V.; Bifulco, P.; Gargiulo, P. Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty. Diagnostics 2020, 10, 815. https://doi.org/10.3390/diagnostics10100815

AMA Style

Ricciardi C, Jónsson H Jr., Jacob D, Improta G, Recenti M, Gíslason MK, Cesarelli G, Esposito L, Minutolo V, Bifulco P, Gargiulo P. Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty. Diagnostics. 2020; 10(10):815. https://doi.org/10.3390/diagnostics10100815

Chicago/Turabian Style

Ricciardi, Carlo; Jónsson, Halldór, Jr.; Jacob, Deborah; Improta, Giovanni; Recenti, Marco; Gíslason, Magnús K.; Cesarelli, Giuseppe; Esposito, Luca; Minutolo, Vincenzo; Bifulco, Paolo; Gargiulo, Paolo. 2020. "Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty" Diagnostics 10, no. 10: 815. https://doi.org/10.3390/diagnostics10100815

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