Artificial Intelligence for Thyroid Nodule Characterization : Where Are We Standing?
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Date
2022-07-10
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Abstract
Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.
Description
Funding Information: The authors would like to thank the support of NSF, CIAN, and Fujitsu. Publisher Copyright: © 2022 by the authors.
Keywords
artificial intelligence, machine learning, thyroid cancer, 3.2 Clinical medicine, 1.1. Scientific article indexed in Web of Science and/or Scopus database, SDG 3 - Good Health and Well-being
Citation
Sorrenti, S, Dolcetti, V, Radzina, M, Bellini, M I, Frezza, F, Munir, K, Grani, G, Durante, C, D'Andrea, V, David, E, Calò, P G, Lori, E & Cantisani, V 2022, 'Artificial Intelligence for Thyroid Nodule Characterization : Where Are We Standing?', Cancers, vol. 14, no. 14, 3357. https://doi.org/10.3390/cancers14143357