Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules

Abdilkadir Bozkuş; Yeliz Başar; Koray Güven

doi:10.4274/dir.2025.253428

September 1, 2025Diagnostic and Interventional RadiologyOpen Access

Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules

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Key Points

AI-TIRADS exhibited superior diagnostic performance compared to traditional methods, enhancing accuracy in identifying thyroid nodules.
The evaluation revealed that AI-TIRADS outperformed established TIRADS in overall diagnostic values across varied cases.
This analysis employed a comparative approach using diagnostic performance metrics for AI-TIRADS and standard classification systems.
The findings support the potential integration of AI-TIRADS in clinical practices, highlighting its value over current standards.

Implication

Analysis shows AI-TIRADS improves diagnostic performance for thyroid nodules, suggesting its utility alongside established systems.

Abstract

This study aimed to evaluate and compare the diagnostic performance of various Thyroid Imaging Reporting and Data Systems (TIRADS), with a particular focus on the artificial intelligence-based TIRADS (AI-TIRADS), in characterizing thyroid nodules.

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Cite This Study

Bozkuş et al. (2025) studied this question.

synapsesocial.com/papers/68bb49db6d6d5674bcd0033b https://doi.org/10.4274/dir.2025.253428

View Full Paper

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Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules

Key Points

Implication

Abstract

Discussion

Cite This Study

Comparison of the diagnostic performance of the artificial intelligence-based TIRADS algorithm with established classification systems for thyroid nodules

Key Points

Implication

Abstract

Discussion

Cite This Study

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