Improving prosthetic selection and predicting BMD from biometric measurements in patients receiving total hip arthroplasty

Carlo Ricciardi*, Halldór Jónsson, Deborah Jacob, Giovanni Improta, Marco Recenti, Magnús Kjartan Gíslason, Giuseppe Cesarelli, Luca Esposito, Vincenzo Minutolo, Paolo Bifulco, Paolo Gargiulo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Article number0815
Issue number10
Publication statusPublished - 14 Oct 2020

Bibliographical note

This research was supported jointly by the University of Reykjavik and the Icelandic National Hospital (Landspítali Scientific Fund; PI: Paolo Gargiulo; Title: Bone modeling in patients undergoing THA; Project Number: A-2014-072) with additional funding support from Rannís (Rannís Icelandic Research Fund (Rannsóknasjodur); PI: Paolo Gargiulo; Title: Clinical evaluation score for Total Hip Arthroplasty planning and postoperative assessment; Project Number: 152368-051). The authors wish to thank the A&C M-C Foundation of Translational Myology, Padova, Italy for sponsorship the publication.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Other keywords

  • Clinical decision making
  • Database analyses
  • Electromyography
  • Machine learning
  • Total hip arthroplasty
  • Mjaðmaaðgerðir
  • Liðskiptaaðgerðir
  • Arthroplasty, Replacement, Hip
  • Clinical Decision-Making


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