ArticleJournal of thyroid research2020
Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions.
Article in Journal of thyroid research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Thyroid disease detection using enhanced extreme learning machine based on drop-connect method.Scientific reports · 2026Article
- Annotation-free genetic mutation estimation of thyroid cancer using cytological slides from multi-centers.Diagnostic pathology · 2025Article
- Challenges and Opportunities in Cytopathology Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Review
- Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model.Journal of pathology and translational medicine · 2024Article
- Artificial-Intelligence-Assisted Detection of Metastatic Colorectal Cancer Cells in Ascitic Fluid.Cancers · 2024Article
- Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology.The American journal of 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
- Applications of machine and deep learning to thyroid cytology and histopathology: a review.Frontiers in oncology · 2023Review
- Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review.Cancers · 2022Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions.
resultsThe RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed.
conclusionAI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.
Identifiers
What OpenQuestion holds
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.