ArticleUltrasonography (Seoul, Korea)2021
Applications of machine learning and deep learning to thyroid imaging: where do we stand?
Article in Ultrasonography (Seoul, Korea), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Computer-Aided Diagnosis in the Evaluation of Thyroid Nodules: A Study of Intra- And Inter-Rater Reliability and Agreement.Head & neck · 2026Article
- Artificial intelligence-based multimodal multitask analysis of thyroid ultrasound image features predicts thyroid cancer: a multicenter study.JNCI cancer spectrum · 2026Article
- Revolutionizing thyroid nodule diagnosis in Hashimoto's thyroiditis: AI-driven radiomics and deep learning model.International journal of surgery (London, England) · 2025Article
- Review
- Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach.European radiology · 2025Article
- Comparative Analysis of Diagnostic Performance Between Elastography and AI-Based S-Detect for Thyroid Nodule Detection.Diagnostics (Basel, Switzerland) · 2025Article
- EUS in children with eosinophilic oesophagitis - a new method of measuring oesophageal total wall thickness area. An artificial intelligence application feasibility study. A pilot study.Journal of ultrasonography · 2024Article
- Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics.Metabolites · 2023Article
- Machine Learning Model as a Useful Tool for Prediction of Thyroid Nodules Histology, Aggressiveness and Treatment-Related Complications.Journal of personalized medicine · 2023Article
- Development of a machine learning-based fine-grained risk stratification system for thyroid nodules using predefined clinicoradiological features.European radiology · 2023Article
- Quantitative Diagnosis Progress of Ultrasound Imaging Technology in Thyroid Diffuse Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data.Journal of research in health sciences · 2022Article
- Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto's thyroiditis.Frontiers in oncology · 2022Article
- Diagnosis of Thyroid Nodules Based on Image Enhancement and Deep Neural Networks.Computational intelligence and neuroscience · 2022Article
- Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound.Life (Basel, Switzerland) · 2021Article
- Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.Medicina (Kaunas, Lithuania) · 2021Article
- A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules.Frontiers in oncology · 2021Article
- Artificial intelligence for ultrasonography: unique opportunities and challenges.Ultrasonography (Seoul, Korea) · 2021Article
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.Cancers · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasonography (US) is the primary diagnostic tool used to assess the risk of malignancy and to inform decision-making regarding the use of fine-needle aspiration (FNA) and postFNA management in patients with thyroid nodules. However, since US image interpretation is operator-dependent and interobserver variability is moderate to substantial, unnecessary FNA and/or diagnostic surgery are common in practice. Artificial intelligence (AI)-based computeraided diagnosis (CAD) systems have been introduced to help with the accurate and consistent interpretation of US features, ultimately leading to a decrease in unnecessary FNA. This review provides a developmental overview of the AI-based CAD systems currently used for thyroid nodules and describes the future developmental directions of these systems for the personalized and optimized management of thyroid nodules.
Indexed as
Identifiers
What OpenQuestion holds
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.