Hip prosthesis failure prediction through radiological deep sequence learning.

Journal: International journal of medical informatics
PMID: 39884035

Abstract

BACKGROUND: Existing deep learning studies for the automated detection of hip prosthesis failure only consider the last available radiographic image. However, using longitudinal data is thought to improve the prediction, by combining temporal and spatial components. The aim of this study is to develop artificial intelligence models for predicting hip implant failure from multiple subsequent plain radiographs.

Authors

  • Francesco Masciulli
    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.
  • Alessia Lindemann
    Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Golgi 39, 20131 Milan, 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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