ArticleEndocrine2023
Clinical evaluation of malignancy diagnosis of rare thyroid carcinomas by an artificial intelligent automatic diagnosis system.
Article in Endocrine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 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
- Improving AI models for rare thyroid cancer subtype by text guided diffusion models.Nature communications · 2025Article
- The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice.BMC medicine · 2024Article
- Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population.Nature communications · 2024Article
- US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration?European radiology · 2024Article
- Comparative Analysis of AI-SONICTM Thyroid System and Six Thyroid Risk Stratification Guidelines in Papillary Thyroid Cancer: A Retrospective Cohort Study.Therapeutics and clinical risk management · 2024Article
- Differential regional importance mapping for thyroid nodule malignancy prediction with potential to improve needle aspiration biopsy sampling reliability.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeTo evaluate the application value of a generally trained artificial intelligence (AI) automatic diagnosis system in the malignancy diagnosis of rare thyroid carcinomas, such as follicular thyroid carcinoma, medullary thyroid carcinoma, primary thyroid lymphoma and anaplastic thyroid carcinoma and compare the diagnostic performance with radiologists of different experience levels.
methodsWe retrospectively studied 342 patients with 378 thyroid nodules that included 196 rare malignant nodules by using postoperative pathology as the gold standard, and compared the diagnostic performances of three radiologists (one junior, one mid-level, one senior) and that of AI automatic diagnosis system.
resultsThe accuracy of the AI system in malignancy diagnosis was 0.825, which was significantly higher than that of all three radiologists and higher than the best radiologist in this study by a margin of 0.097 with P-value of 2.252 × 10
conclusionsThe generally trained AI automatic diagnosis system showed high accuracy in the differential diagnosis of begin nodules and rare malignancy nodules. It may assist radiologists for screening of rare malignancy nodules that even senior radiologists are not acquainted with.
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