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Application of machine learning methods to guide patient management by predicting the risk of malignancy of Bethesda III-V thyroid nodules.

Grégoire D'AndréaJocelyn GalLoïc MandineOlivier DassonvilleClair VandersteenNicolas GuevaraLaurent CastilloGilles PoissonnetDorian CuliéRoxane ElaldiJérôme SariniAnne DecotteClaire RenaudSébastien VergezRenaud SchiappaEmmanuel ChamoreyYann ChâteauAlexandre Bozec
Published in: European journal of endocrinology (2023)
Our ML models performed well in predicting the nature of Bethesda III-V TN. In addition, our freely available online nomogram helped to refine the RM, identifying low-risk TN that may benefit from surveillance in up to a third of ITN, and thus may reduce the number of unnecessary surgeries.
Keyphrases
  • machine learning
  • fine needle aspiration
  • public health
  • case report
  • ultrasound guided
  • social media
  • health information
  • artificial intelligence
  • lymph node metastasis
  • big data
  • squamous cell carcinoma
  • deep learning