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Related Concept Videos

Graves' Disease I: Introduction01:28

Graves' Disease I: Introduction

Graves' disease is an autoimmune disorder that causes hyperthyroidism, or overactivity of the thyroid gland. It results from autoantibodies called thyroid-stimulating immunoglobulins (TSIs), which bind to thyroid-stimulating hormone (TSH) receptors, leading to overstimulation of hormone production and a hypermetabolic state.EtiologyAlthough considered idiopathic, Graves’ disease has well-established contributing factors. There is a strong genetic component, with increased prevalence in...

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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules-a systematic review

Karishma Jassal1,2, Melissa Edwards1, Afsaneh Koohestani1,2

  • 1Monash University Endocrine Surgery Unit, Alfred Hospital, Melbourne, VIC, Australia.

Frontiers in Endocrinology
|May 20, 2025
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Summary

Artificial intelligence (AI) shows promise for classifying indeterminate thyroid nodules (ITNs) but requires further development for clinical use. Current AI models demonstrate moderate-to-good performance but need robust validation before widespread adoption in pre-operative diagnosis.

Keywords:
artificial intelligencemachine learningmeta - analysisthyroid cancerthyroid nodule - diagnosis

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Thyroidology

Background:

  • Indeterminate thyroid nodules (ITNs) present a diagnostic challenge in clinical thyroidology, impacting surgical decision-making.
  • Genomic sequencing classifiers (GSCs) aid in ITN diagnosis but face barriers of cost and accessibility, creating an equity gap.
  • Artificial intelligence (AI) tools are increasingly studied for thyroid ultrasonography (USG) classification, aiming to improve pre-operative diagnosis.

Purpose of the Study:

  • To systematically review and analyze the current evidence on AI tools for diagnosing ITNs without relying on GSCs.
  • To assess the diagnostic accuracy and clinical applicability of AI models in the pre-operative assessment of ITNs.
  • To identify limitations and areas for future development in AI for ITN diagnosis.

Main Methods:

  • A systematic literature search was conducted on PubMed, Google Scholar, and Scopus up to February 18, 2025.
  • Included studies evaluated the diagnostic accuracy of AI for ITNs, with a focus on models not using GSC.
  • A meta-analysis was performed on area under the curve (AUC) results from included AI models, adhering to Cochrane Collaboration guidelines.

Main Results:

  • Seven studies presented 20 AI models, including radiological, natural language processing, and cytology-focused approaches.
  • The pooled meta-analysis of 15 models yielded a combined AUC of 0.82, indicating moderate-to-good classification performance for machine learning (ML) and deep learning (DL) architectures.
  • Substantial heterogeneity was noted, particularly in DL models (AUC=0.85), while ML models showed minimal heterogeneity (AUC=0.75). Meta-regression suggested potential publication bias or systematic differences in model design and validation.

Conclusions:

  • AI demonstrates significant potential to aid clinical decision-making for ITNs, offering a promising alternative to GSCs.
  • Current AI models, while performing adequately, are not yet suitable for widespread clinical implementation due to limitations in performance and lack of robust external validation.
  • Further research and development are essential to enhance AI model robustness, generalizability, and clinical utility in the pre-operative diagnosis of thyroid nodules.