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Review
. 2025 Apr 2;14(7):2422.
doi: 10.3390/jcm14072422.

Role of Artificial Intelligence in Thyroid Cancer Diagnosis

Alessio Cece  1 , Massimo Agresti  1 , Nadia De Falco  1 , Pasquale Sperlongano  1 , Giancarlo Moccia  1 , Pasquale Luongo  1 , Francesco Miele  1 , Alfredo Allaria  1 , Francesco Torelli  1 , Paola Bassi  1 , Antonella Sciarra  1 , Stefano Avenia  2 , Paola Della Monica  3 , Federica Colapietra  3 , Marina Di Domenico  3 , Ludovico Docimo  4 , Domenico Parmeggiani  1
Affiliations
Review

Role of Artificial Intelligence in Thyroid Cancer Diagnosis

Alessio Cece et al. J Clin Med. .

Abstract

The progress of artificial intelligence (AI), particularly its core algorithms-machine learning (ML) and deep learning (DL)-has been significant in the medical field, impacting both scientific research and clinical practice. These algorithms are now capable of analyzing ultrasound images, processing them, and providing outcomes, such as determining the benignity or malignancy of thyroid nodules. This integration into ultrasound machines is referred to as computer-aided diagnosis (CAD). The use of such software extends beyond ultrasound to include cytopathological and molecular assessments, enhancing the estimation of malignancy risk. AI's considerable potential in cancer diagnosis and prevention is evident. This article provides an overview of AI models based on ML and DL algorithms used in thyroid diagnostics. Recent studies demonstrate their effectiveness and diagnostic role in ultrasound, pathology, and molecular fields. Notable advancements include content-based image retrieval (CBIR), enhanced saliency CBIR (SE-CBIR), Restore-Generative Adversarial Networks (GANs), and Vision Transformers (ViTs). These new algorithms show remarkable results, indicating their potential as diagnostic and prognostic tools for thyroid pathology. The future trend points to these AI systems becoming the preferred choice for thyroid diagnostics.

Keywords: artificial intelligence in thyroid nodule diagnosis; deep learning; genomic sequencing; machine learning; radiomics; thyroid cancer.

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Conflict of interest statement

The authors declare that there is no conflicts of interest.

Figures

Figure 1
Figure 1
Three-dimensional representation of an ultrasound image of a thyroid nodule [32].
See this image and copyright information in PMC

References

    1. Pizzato M., Li M., Vignat J., Laversanne M., Singh D., La Vecchia C., Vaccarella S. The epidemiological landscape of thyroid cancer worldwide: GLOBOCAN estimates for incidence and mortality rates in 2020. Lancet Diabetes Endocrinol. 2022;10:264–272. - PubMed
    1. AME NEWS No. 66-August 2022. European Society of Endocrinology; Bristol, UK: 2022.
    1. Locati L.D., Bernardo G. Cancer Numbers in Italy 2022. National Centre for Disease Prevention and Health Promotion; Milan, Italy: 2022. Thyroid; p. 85. AIOM; AIRT; AIOM Foundation; ONS; SIAPEC-IAP.
    1. Barp S., Grani G. Thyroid Cancer Guidelines. 2017 ed. AIOM; Milan, Italy: 2017. Artificial intelligence in the diagnosis of the thyroid nodule; updated October 2019.
    1. Sollini M., Cozzi L., Chiti A., Kirienko M. Texture analysis and machine learning to characterise suspected thyroid nodules and differentiated thyroid cancer: Where do we stand? Eur. J. Radiol. 2018;99:1–8. - PubMed

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