A narrative review of deep learning in thyroid imaging: current progress and future prospects.

ABSTRACT:

Background and objective

Deep learning (DL) has contributed substantially to the evolution of image analysis by unlocking increased data and computational power. These DL algorithms have further facilitated the growing trend of implementing precision medicine, particularly in areas of diagnosis and therapy. Thyroid imaging, as a routine means to screening for thyroid diseases on large-scale populations, is a massive data source for the development of DL models. Thyroid disease is a global health problem and involves structural and functional changes. The objective of this study was to evaluate the general rules and future directions of DL networks in thyroid medical image analysis through a review of original articles published between 2018 and 2023.

Methods

We searched for

SUBMITTER: Yang WT 

PROVIDER: S-EPMC10895129 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature