ReviewQuantitative imaging in medicine and surgery2024
A narrative review of deep learning in thyroid imaging: current progress and future prospects.
Review in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Thyroid disease detection using enhanced extreme learning machine based on drop-connect method.Scientific reports · 2026Article
- Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?Updates in surgery · 2026Article
- Experience-dependent integration of a commercial AI system with C-TIRADS for thyroid nodule assessment: a retrospective single-center study.Frontiers in endocrinology · 2026Article
- Machine learning diagnostic model integrating ultrasound features and immune-inflammatory biomarkers for identifying papillary thyroid carcinoma in patients with Hashimoto's thyroiditis.Frontiers in immunology · 2026Article
- [Research progress on deep learning-based computer-aided diagnosis of thyroid nodules using ultrasound imaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Polarization-Interference Jones Matrix Sensors of Layer-by-Layer Scanning of Polycrystalline Dehydrated Blood Films. Fundamental and Applied Aspects.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence-enhanced ultrasound imaging for thyroid nodule detection and malignancy classification: a study on YOLOv11.Quantitative imaging in medicine and surgery · 2025Article
- Combining curriculum learning and weakly supervised attention for enhanced thyroid nodule assessment in ultrasound imaging.Quantitative imaging in medicine and surgery · 2025Article
- Enhancing diagnostic precision for thyroid C-TIRADS category 4 nodules: a hybrid deep learning and machine learning model integrating grayscale and elastographic ultrasound features.Quantitative imaging in medicine and surgery · 2025Article
- Advanced machine learning framework for thyroid cancer epidemiology in Iran through integration of environmental socioeconomic and health system predictors.Scientific reports · 2025Article
- FERTILITY CARE IN LOW- AND MIDDLE- INCOME COUNTRIES: The future use of AI to improve accessibility of assisted reproductive technology in low- and middle-income countries.Reproduction & fertility · 2025Review
- Dual-stage artificial intelligence-powered screening for accurate classification of thyroid nodules: enhancing fine needle aspiration biopsy precision.Quantitative imaging in medicine and surgery · 2025Article
- Diagnosis of thyroid nodules using ultrasound images based on deep learning features: online dynamic nomogram and gradient-weighted class activation mapping.Quantitative imaging in medicine and surgery · 2025Article
- A predictive nomogram of thyroid nodules based on deep learning ultrasound image analysis.Frontiers in endocrinology · 2025Article
- Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
3 authors.
Funding
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.