Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY.

Journal: JCO clinical cancer informatics
PMID: 39657101

Abstract

PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive database access is crucial for improving this initial assessment. Our objective was to develop an automated process using convolutional neural networks (CNNs) to extract and structure biomedical insights from electronic health reports (EHRs) in a large thyroid pathology cohort.

Authors

  • Dorian Culie
    Cervico-facial Oncology Surgical Department, University Institute of Face and Neck, University of Côte d'Azur, Nice, France.
  • Renaud Schiappa
    Department of Epidemiology, Biostatistics and Health Data, Centre Antoine Lacassagne, University of Côte d'Azur, Nice, France.
  • Sara Contu
  • Eva Seutin
    Department of Epidemiology, Biostatistics and Health Data, Centre Antoine Lacassagne, University of Côte d'Azur, Nice, France.
  • Tanguy Pace-Loscos
    Department of Epidemiology, Biostatistics and Health Data, Centre Antoine Lacassagne, University of Côte d'Azur, Nice, France.
  • Gilles Poissonnet
    Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France.
  • Agathe Villarme
    Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France.
  • Alexandre Bozec
    Head and Neck Surgery Department, Antoine Laccassagne Center, 06100 Nice, France.
  • Emmanuel Chamorey
    Department of Epidemiology, Biostatistics and Health Data, Centre Antoine Lacassagne, University of Côte d'Azur, Nice, France.

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