Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks.

Journal: Journal of thermal biology
Published Date: Feb 17, 2023

Abstract

PURPOSE: There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors.

Authors

  • Mar Cañada-Soriano
    Applied Thermodynamics Department (DTRA), Universitat Politècnica de València, Valencia, Spain.
  • Maite Bovaira
    Anaesthesia Department, Hospital Intermutual de Levante, Sant Antoni de Benaixeve, Valencia, Spain.
  • Carles García-Vitoria
    Anaesthesia Department, Hospital Intermutual de Levante, Sant Antoni de Benaixeve, Valencia, Spain.
  • Rosario Salvador-Palmer
    Research Group in Medical Physics (GIFIME), Department of Physiology, University of Valencia, Valencia, Spain.
  • Rosa Cibrián Ortiz de Anda
    Research Group in Medical Physics (GIFIME), Department of Physiology, University of Valencia, Valencia, Spain.
  • David Moratal
    Centre for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.
  • José Ignacio Priego-Quesada
    Research Group in Medical Physics (GIFIME), Department of Physiology, University of Valencia, Valencia, Spain; Research Group in Sports Biomechanics (GIBD), Department of Physical Education and Sports, University of Valencia, Valencia, Spain. Electronic address: j.ignacio.priego@uv.es.

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