Neural network-based assessment of femur stress after hip joint alloplasty

  • Authors:

  • Marcin Korytkowski;Leszek Rutkowski;Rafał Scherer;Arkadiusz Szarek

  • Affiliations:

  • Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Olsztyn Academy of Computer Science and Management, Olsztyn, Poland;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Academy of Management, SWSPiZ, Institute of Information Technology, Łódź, Poland;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Academy of Management, SWSPiZ, Institute of Information Technology, Łódź, Poland;Institute of Metal Working and Forming, Quality Engineering and Bioengineering, Czstochowa University of Technology

  • Venue:
  • ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
  • Year:
  • 2010

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Abstract

Neural networks are a practical tool for solving various problems of approximation, classification, prediction or control. In the paper we use multi-layer perceptrons to determine the character of stress in healthy femur and after endoprosthesoplasty. Inserting metal prosthesis to the bone changes the stress character what can lead to local decalcification and weakening of its strength in certain areas. Dynamic bone load resulting from non-anatomical load can cause fracture in the weak area. Neural network was learned with the data obtained from numerical simulations using the finite element analysis. The input to the network was stress state in twelve points of femur and body mass.