Artificial Intelligence for Thyroid Nodule Characterization : Where Are We Standing?

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dc.contributor.authorSorrenti, Salvatore
dc.contributor.authorDolcetti, Vincenzo
dc.contributor.authorRadzina, Maija
dc.contributor.authorBellini, Maria Irene
dc.contributor.authorFrezza, Fabrizio
dc.contributor.authorMunir, Khushboo
dc.contributor.authorGrani, Giorgio
dc.contributor.authorDurante, Cosimo
dc.contributor.authorD'Andrea, Vito
dc.contributor.authorDavid, Emanuele
dc.contributor.authorCalò, Pietro Giorgio
dc.contributor.authorLori, Eleonora
dc.contributor.authorCantisani, Vito
dc.contributor.institutionRīga Stradiņš University
dc.date.accessioned2022-08-29T12:00:01Z
dc.date.available2022-08-29T12:00:01Z
dc.date.issued2022-07-10
dc.descriptionFunding Information: The authors would like to thank the support of NSF, CIAN, and Fujitsu. Publisher Copyright: © 2022 by the authors.
dc.description.abstractMachine 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.en
dc.description.statusPeer reviewed
dc.format.extent2096052
dc.identifier.citationSorrenti, 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
dc.identifier.doi10.3390/cancers14143357
dc.identifier.issn2072-6694
dc.identifier.urihttps://dspace.rsu.lv/jspui/handle/123456789/9495
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85136388586&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofCancers
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectthyroid cancer
dc.subject3.2 Clinical medicine
dc.subject1.1. Scientific article indexed in Web of Science and/or Scopus database
dc.subjectSDG 3 - Good Health and Well-being
dc.titleArtificial Intelligence for Thyroid Nodule Characterization : Where Are We Standing?en
dc.type/dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/systematicreview

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