Artificial Intelligence in Thyroid Fine Needle Aspiration Biopsies

Acta Cytol. 2021;65(4):324-329. doi: 10.1159/000512097. Epub 2020 Dec 16.


Background: From cell phones to aerospace, artificial intelligence (AI) has wide-reaching influence in the modern age. In this review, we discuss the application of AI solutions to an equally ubiquitous problem in cytopathology - thyroid fine needle aspiration biopsy (FNAB). Thyroid nodules are common in the general population, and FNAB is the sampling modality of choice. The resulting prevalence in the practicing pathologist's daily workload makes thyroid FNAB an appealing target for the application of AI solutions.

Summary: This review summarizes all available literature on the application of AI to thyroid cytopathology. We follow the evolution from morphometric analysis to convolutional neural networks. We explore the application of AI technology to different questions in thyroid cytopathology, including distinguishing papillary carcinoma from benign, distinguishing follicular adenoma from carcinoma and identifying non-invasive follicular thyroid neoplasm with papillary-like nuclear features by key words and phrases. Key Messages: The current literature shows promise towards the application of AI technology to thyroid fine needle aspiration biopsy. Much work is needed to define how this powerful technology will be of best use to the future of cytopathology practice.

Keywords: Artificial intelligence; Machine learning; Thyroid fine needle aspiration biopsy.

Publication types

  • Review

MeSH terms

  • Biopsy, Fine-Needle
  • Diagnosis, Computer-Assisted*
  • Diagnosis, Differential
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Machine Learning*
  • Neural Networks, Computer*
  • Predictive Value of Tests
  • Reproducibility of Results
  • Thyroid Neoplasms / pathology*