ArticleJournal of pathology informatics2018
Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-Needle Aspiration Cytology Smears.
Article in Journal of pathology informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules-a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Multimodal deep learning for papillary thyroid carcinoma diagnosis using ultrasound and cytology.BMC medical imaging · 2026Article
- An early evaluation of MedSigLIP in thyroid cytology: a comparative frozen-encoder benchmark against ImageNet-pretrained encoders.Frontiers in endocrinology · 2026Article
- Cytological Classification Diagnosis for Thyroid Nodules via Multimodal Model Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning neural network of adenocarcinoma detection in effusion cytology.American journal of clinical pathology · 2025Article
- Interpretable AI-assisted diagnosis of papillary thyroid cancer cytopathology using graph neural networks and knowledge graphs.Scientific reports · 2025Article
- [Prospects for the application of convolutional neural networks in the cytological diagnosis of thyroid nodules].Problemy endokrinologii · 2025Review
- Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy.Scientific reports · 2025Article
- Recent topics on thyroid cytopathology: reporting systems and ancillary studies.Journal of pathology and translational medicine · 2025Article
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- Role of Artificial Intelligence in Thyroid Cancer Diagnosis.Journal of clinical medicine · 2025Review
- Breaking Barriers in Thyroid Cytopathology: Harnessing Deep Learning for Accurate Diagnosis.Bioengineering (Basel, Switzerland) · 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
- Challenges and Opportunities in Cytopathology Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Review
- Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network.Computational and structural biotechnology journal · 2025Article
- AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions.Journal of clinical medicine · 2024Review
- Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.Scientific reports · 2024Article
- Article
- Artificial intelligence assisted diagnosis of early tc markers and its application.Discover oncology · 2024Review
- Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model.Journal of pathology and translational medicine · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionFine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology.
aimThe aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears. SUBJECTS AND
methodsAn ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 - nonpapillary carcinoma and 21 - papillary carcinoma, each photographed at two magnifications ×10 and ×40).
resultsThe performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback.
conclusionWith further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.
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