ArticleThe American journal of pathology2023
Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology.
Article in The American journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 2026Review
- The role of artificial intelligence in thyroid cytology of indeterminate nodules: from digital cytology to multimodal precision triage.Frontiers in endocrinology · 2026Review
- High efficiency classification of thyroid cytopathological images based on knowledge distillation and vision transformer.Scientific reports · 2025Article
- [Prospects for the application of convolutional neural networks in the cytological diagnosis of thyroid nodules].Problemy endokrinologii · 2025Review
- Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.Endocrine · 2025Article
- Study on the Transformation Process of Thyroid Fine-Needle Aspiration Liquid-Based Cytology to Whole-Slide Image.Cytopathology : official journal of the British Society for Clinical Cytology · 2025Article
- Quantitative analysis of studies that use artificial intelligence on thyroid cancer: a 20-year bibliometric analysis.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
Abstract
Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification.
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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.