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Artificial Intelligence in Thyroid Field-A Comprehensive Review.
Bini, Fabiano; Pica, Andrada; Azzimonti, Laura; Giusti, Alessandro; Ruinelli, Lorenzo; Marinozzi, Franco; Trimboli, Pierpaolo.
Afiliação
  • Bini F; Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, 00184 Rome, Italy.
  • Pica A; Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, 00184 Rome, Italy.
  • Azzimonti L; Dalle Molle Institute for Artificial Intelligence (IDSIA), Università della Svizzera Italiana (USI), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Polo Universitario Lugano-Campus Est, 6962 Lugano-Viganello, Switzerland.
  • Giusti A; Dalle Molle Institute for Artificial Intelligence (IDSIA), Università della Svizzera Italiana (USI), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Polo Universitario Lugano-Campus Est, 6962 Lugano-Viganello, Switzerland.
  • Ruinelli L; Information and Communications Technology, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland.
  • Marinozzi F; Clinical Trial Unit, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland.
  • Trimboli P; Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, 00184 Rome, Italy.
Cancers (Basel) ; 13(19)2021 Sep 22.
Article em En | MEDLINE | ID: mdl-34638226
Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article