ArticleFrontiers in endocrinology2022
An artificial intelligence ultrasound system's ability to distinguish benign from malignant follicular-patterned lesions.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.The Journal of clinical endocrinology and metabolism · 2024Pooled it
- Preliminary machine-learning model with clinical, US, and CEUS features for the diagnosis of thyroid follicular-patterned lesions.BMC medical imaging · 2026Article
- Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.JMIR formative research · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 2026Article
- Deep learning-based differentiation of benign and malignant thyroid follicular neoplasms on multiscale intraoperative frozen pathological images: A multicenter diagnostic study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Clinical Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice.BMC medicine · 2024Article
- Ultrasound characteristics of follicular and parafollicular thyroid neoplasms: diagnostic performance of artificial neural network.Thyroid research · 2023Article
- Multi-Omics and Management of Follicular Carcinoma of the Thyroid.Biomedicines · 2023Review
- Differential regional importance mapping for thyroid nodule malignancy prediction with potential to improve needle aspiration biopsy sampling reliability.Frontiers in oncology · 2023Article
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of follicular-patterned thyroid lesions (FPTL), including follicular thyroid carcinoma (FTC), adenomatoid hyperplasia nodule (AHN) and follicular thyroid adenoma (FTA) and compare the diagnostic performance with radiologists of different experience levels. Methods: We retrospectively reviewed 607 patients with 699 thyroid nodules that included 168 malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, two senior) and that of AI automatic diagnosis system in malignancy diagnosis of FPTL in terms of sensitivity, specificity and accuracy, respectively. Pairwise t-test was used to evaluate the statistically significant difference. Results: The accuracy of the AI system in malignancy diagnosis was 0.71, which was higher than the best radiologist in this study by a margin of 0.09 with a p-value of 2.08×10 Conclusions: The generally trained AI automatic diagnosis system can potentially assist radiologists for distinguishing FTC from other FPTL cases that share poorly distinguishable ultrasonographical features.
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