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Updated: Jun 30, 2026
04:23
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
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A narrative review of deep learning in thyroid imaging: current progress and future prospects
Wan-Ting Yang1, Bu-Yun Ma1, Yang Chen1
1Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, China.
Quantitative Imaging in Medicine and Surgery
|February 28, 2024
Summary
Deep learning (DL) enhances thyroid image analysis for better diagnosis and treatment. This review explores DL applications in thyroid disease, guiding future precision medicine in endocrinology.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Endocrinology
Background:
- Deep learning (DL) significantly advances image analysis, driving precision medicine in diagnosis and therapy.
- Thyroid imaging provides extensive data for developing DL models to address global thyroid disease prevalence.
Purpose of the Study:
- To review general rules and future directions of DL networks in thyroid medical image analysis.
- To evaluate original articles published between 2018 and 2023.
Main Methods:
- Systematic literature search across PubMed, Web of Science, and Google Scholar.
- Keywords included AI/DL, thyroid diseases, and thyroid nodule/carcinoma.
- Analysis of English-language articles from April 2018 to September 2023.
Main Results:
- DL computer vision tasks in thyroid imaging encompass classification, segmentation, and detection.
- Current DL applications include managing thyroid nodules/carcinoma, evaluating metastasis risk, and discriminating functional diseases.
Conclusions:
- DL is poised to improve thyroid image quality and assessment precision.
- DL is expected to increase diagnostic accuracy and inform clinical decision-making for thyroid diseases.
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Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The iodine is then...
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