Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data.

Journal: International journal of medical informatics
PMID:

Abstract

BACKGROUND: Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data.

Authors

  • Sk Imran Hossain
    Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ENSMSE, LIMOS, France.
  • Jocelyn de Goër de Herve
    Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, France; Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, France.
  • David Abrial
    Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, France; Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, France.
  • Richard Emilion
    Université d'Orléans, Institut Denis Poisson, France.
  • Isabelle Lebert
    Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, France; Université de Lyon, INRAE, VetAgro Sup, UMR EPIA, France.
  • Yann Frendo
    Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ENSMSE, LIMOS, France; Université Clermont Auvergne, INRAE, VetAgro Sup, UMR EPIA, France.
  • Delphine Martineau
    Infectious and Tropical Diseases Department, CHU Clermont-Ferrand, France.
  • Olivier Lesens
    Infectious and Tropical Diseases Department, CRIOA, CHU Clermont-Ferrand, France; UMR CNRS 6023, Laboratoire Microorganismes: Génome Environnement (LMGE), UCA, France.
  • Engelbert Mephu Nguifo
    Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ENSMSE, LIMOS, France. Electronic address: engelbert.mephu_nguifo@uca.fr.