A narrative review of deep learning in thyroid imaging: current progress and future prospects.
리뷰
1/5 보강
[BACKGROUND AND OBJECTIVE] Deep learning (DL) has contributed substantially to the evolution of image analysis by unlocking increased data and computational power.
APA
Yang WT, Ma BY, Chen Y (2024). A narrative review of deep learning in thyroid imaging: current progress and future prospects.. Quantitative imaging in medicine and surgery, 14(2), 2069-2088. https://doi.org/10.21037/qims-23-908
MLA
Yang WT, et al.. "A narrative review of deep learning in thyroid imaging: current progress and future prospects.." Quantitative imaging in medicine and surgery, vol. 14, no. 2, 2024, pp. 2069-2088.
PMID
38415152
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 English-language articles published between April 2018 and September 2023 in the databases of PubMed, Web of Science, and Google Scholar. The keywords used in the search included artificial intelligence or DL, thyroid diseases, and thyroid nodule or thyroid carcinoma.
[KEY CONTENT AND FINDINGS] The computer vision tasks of DL in thyroid imaging included classification, segmentation, and detection. The current applications of DL in clinical workflow were found to mainly include management of thyroid nodules/carcinoma, risk evaluation of thyroid cancer metastasis, and discrimination of functional thyroid diseases.
[CONCLUSIONS] DL is expected to enhance the quality of thyroid images and provide greater precision in the assessment of thyroid images. Specifically, DL can increase the diagnostic accuracy of thyroid diseases and better inform clinical decision-making.
[METHODS] We searched for English-language articles published between April 2018 and September 2023 in the databases of PubMed, Web of Science, and Google Scholar. The keywords used in the search included artificial intelligence or DL, thyroid diseases, and thyroid nodule or thyroid carcinoma.
[KEY CONTENT AND FINDINGS] The computer vision tasks of DL in thyroid imaging included classification, segmentation, and detection. The current applications of DL in clinical workflow were found to mainly include management of thyroid nodules/carcinoma, risk evaluation of thyroid cancer metastasis, and discrimination of functional thyroid diseases.
[CONCLUSIONS] DL is expected to enhance the quality of thyroid images and provide greater precision in the assessment of thyroid images. Specifically, DL can increase the diagnostic accuracy of thyroid diseases and better inform clinical decision-making.