Thyroid nodules with indeterminate cytology represent a significant diagnostic challenge, accounting for approximately one-third of all nodules undergoing fine needle aspiration and cytological evaluation. These nodules are classified into low-risk and high-risk categories, with varying predictive values for malignancy. However, despite advances in cytopathology, the overall malignancy rate remains relatively low, leading to a considerable number of false-positive results, largely influenced by operator-dependent variability. Traditional molecular testing, including mutational analyses of RAF, BRAF, and TERT genes, can improve diagnostic accuracy but is often restricted to specialized centers due to high costs and limited accessibility. Given these limitations, there is a growing interest in leveraging artificial intelligence (AI) to enhance the diagnostic precision of indeterminate thyroid cytology.
This Research Topic aims to explore the integration of AI in refining the diagnosis and management of thyroid nodules with indeterminate cytology. AI-powered tools, such as machine learning, deep learning, and neural networks, have demonstrated substantial progress in improving the detection of malignancy in thyroid nodules based on ultrasonographic and cytopathological features. Digital pathology and AI-driven image analysis can minimize operator-dependent errors, enhance pattern recognition, and optimize malignancy risk stratification. Despite these advancements, challenges remain regarding the standardization and clinical implementation of AI-based approaches. This Research Topic will focus on the latest breakthroughs in AI applications for indeterminate cytology, addressing current limitations and potential solutions to enhance diagnostic accuracy and clinical decision-making.
We invite researchers and clinicians to contribute original research articles, systematic reviews, narrative reviews, and perspectives on AI applications in thyroid cytology. Submissions may cover, but are not limited to, the following themes:
- AI-assisted ultrasonographic analysis for indeterminate thyroid nodules
- Digital pathology and automated cytological classification
- Machine learning and deep learning models for malignancy risk assessment
- Integration of molecular markers with AI-driven diagnostic algorithms
- Challenges and ethical considerations in AI implementation in cytology
- Comparative studies between AI-driven and traditional diagnostic approaches
By compiling high-quality contributions, this Research Topic aims to provide a comprehensive overview of the evolving role of AI in improving the management of indeterminate thyroid nodules. We welcome submissions from experts in endocrinology, pathology, bioinformatics, and AI to foster interdisciplinary discussions and advance clinical applications.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Clinical Trial
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
