Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development, validation and explainability analysis.

Journal: International journal of medical informatics
PMID: 37220702

Abstract

AIM: Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a combined deep learning (DL) and machine learning (ML) approach to automatically detect hip prosthetic failure from conventional plain radiographs.

Authors

  • Federico Muscato
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, MI, Italy.
  • Anna Corti
    Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.
  • Francesco Manlio Gambaro
    Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy.
  • Katia Chiappetta
    Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy.
  • Mattia Loppini
    Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, MI, Italy; IRCCS Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, MI, Italy; Fondazione Livio Sciutto Ricerca Biomedica in Ortopedia-ONLUS, Via A. Magliotto 2, 17100 Savona, SV, Italy.
  • Valentina D A Corino
    Department of Electronics, Information and Bioengineering (DEIB), Politecnico Di Milano, Via Golgi 39, 20133, Milan, Italy.

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