Artificial intelligence in thyroid field

Bini, Fabiano; Pica, Andrada; Azzimonti, Laura; Giusti, Alessandro; Ruinelli, Lorenzo; Marinozzi, Franco; Trimboli, Pierpaolo

doi:10.3390/cancers13194740

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Journal article

Artificial intelligence in thyroid field : a comprehensive review

Bini, Fabiano Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, Rome, Italy
Pica, Andrada Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, Rome, Italy
Azzimonti, Laura Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
Giusti, Alessandro Istituto Dalle Molle di studi sull'intelligenza artificiale (IDSIA), Facoltà di scienze informatiche, Università della Svizzera italiana, Svizzera
Ruinelli, Lorenzo Information and Communications Technology, Ente Ospedaliero Cantonale, Bellinzona, Switzerland - Clinical Trial Unit, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
Marinozzi, Franco Department of Mechanical and Aerospace Engineering, Sapienza-University of Rome, Rome, Italy
Trimboli, Pierpaolo Servizio di Endocrinologia e Diabetologia, Ospedale Regionale di Lugano e Mendrisio, Ente Ospedaliero Cantonale, Lugano, Switzerland - Faculty of Biomedical Sciences, Università della Svizzera italiana, Switzerland

22.09.2021

Published in:

Cancers. - MDPI. - 2021, vol. 13, no. 19, p. 18

English 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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