TY - JOUR
T1 - Artificial Intelligence for Thyroid Nodule Characterization
T2 - Where Are We Standing?
AU - Sorrenti, Salvatore
AU - Dolcetti, Vincenzo
AU - Radzina, Maija
AU - Bellini, Maria Irene
AU - Frezza, Fabrizio
AU - Munir, Khushboo
AU - Grani, Giorgio
AU - Durante, Cosimo
AU - D'Andrea, Vito
AU - David, Emanuele
AU - Calò, Pietro Giorgio
AU - Lori, Eleonora
AU - Cantisani, Vito
N1 - Funding Information:
The authors would like to thank the support of NSF, CIAN, and Fujitsu.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/7/10
Y1 - 2022/7/10
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - machine learning
KW - thyroid cancer
UR - http://www.scopus.com/inward/record.url?scp=85136388586&partnerID=8YFLogxK
U2 - 10.3390/cancers14143357
DO - 10.3390/cancers14143357
M3 - Review article
C2 - 35884418
SN - 2072-6694
VL - 14
JO - Cancers
JF - Cancers
IS - 14
M1 - 3357
ER -
