Automatic Annotation Tool to Support Supervised Machine Learning for Scaphoid Fracture Detection.

Journal: Studies in health technology and informatics
PMID: 30306938

Abstract

The aim of this work is to develop and validate an automatic annotation tool for the detection and bone localization of scaphoid fractures in radiology reports. To achieve this goal, a rule-based method using a Natural Language Processing (NLP) tool was applied. Finite state automata were constructed to detect, classify and annotate reports. An evaluation of the method on a manually annotated dataset has shown 96,8% of total match.

Authors

  • Vasiliki Foufi
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.
  • Sébastien Lanteri
    ESIEE Paris.
  • Christophe Gaudet-Blavignac
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.
  • Pascal Remy
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.
  • Xavier Montet
    Service of Radiology, Department of Diagnostics, Geneva University Hospital, Rue Gabrielle Perret-Gentil 4, 1211 Geneva 14, Switzerland (S.M., J.H., S.P.M., X.M.); Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland (S.M., J.H., M.G.P., S.P.M., D.V.D.V., X.M.); and Institute of Bioengineering/Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland (S.M., M.G.P., D.V.D.V.).
  • Christian Lovis
    Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.

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