ArticleArtificial intelligence in medicine2020
Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning.
Article in Artificial intelligence in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- [Frozen section in oncologic endocrine surgery].Chirurgie (Heidelberg, Germany) · 2025Pooled it
- PathoTiroid dataset: Indonesian collection (PTIC)-a histopathology image dataset for papillary thyroid carcinoma.BMC research notes · 2026Article
- Establishment and clinical performance evaluation of the thyroglobulin colloidal gold immunochromatographic assay.Frontiers in oncology · 2026Article
- Advanced AI-Powered System for Comprehensive Thyroid Cancer Detection and Malignancy Risk Assessment.Life (Basel, Switzerland) · 2025Article
- Machine learning is changing osteoporosis detection: an integrative review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- 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
- Deep learning for multitask prediction on thyroid nodule frozen sections.Frontiers in oncology · 2025Article
- External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study.Journal of pathology informatics · 2024Article
- Pathology diagnosis of intraoperative frozen thyroid lesions assisted by deep learning.BMC cancer · 2024Article
- A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.Nature communications · 2024Article
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China.Cancer medicine · 2024Article
- Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023Article
- The value of deep neural networks in the pathological classification of thyroid tumors.Diagnostic pathology · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update.Cancers · 2023Review
- Trends in AI-powered Classification of Thyroid Neoplasms Based on Histopathology Images - a Systematic Review.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023Article
- Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules.The Journal of international medical research · 2022Article
- CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology.Software impacts · 2021Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we propose a rule-based system to differentiate thyroid nodules from intraoperative frozen sections using deep learning techniques. The proposed system consists of three components: (1) automatically locating tissue regions in the whole slide images (WSIs), (2) splitting located tissue regions into patches and classifying each patch into predefined categories using convolutional neural networks (CNN), and (3) integrating predictions of all patches to form the final diagnosis with a rule-based system. To be specific, we fine-tune the InceptionV3 model for thyroid patch classification by replacing the last fully connected layer with three outputs representing the patch's probabilities of being benign, uncertain, or malignant. Moreover, we design a rule-based protocol to integrate patches' predictions to form the final diagnosis, which provides interpretability for the proposed system. On 259 testing slides, the system correctly predicts 95.3% (61/64) of benign nodules and 96.7% (148/153) of malignant nodules, and classify 16.2% (42/259) slides as uncertain, including 19 benign and 16 malignant slides, which are a sufficiently small number to be manually examined by pathologists or fully processed through permanent sections. Besides, the system allows the localization of suspicious regions along with the diagnosis. A typical whole slide image, with 80, 000 × 60, 000 pixels, can be diagnosed within 1 min, thus satisfying the time requirement for intraoperative diagnosis. To the best of our knowledge, this is the first study to apply deep learning to diagnose thyroid nodules from intraoperative frozen sections. The code is released at https://github.com/PingjunChen/ThyroidRule.
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