Artificial intelligence (AI) methods for evaluating thyroid nodules on ultrasound have been
widely described in the literature, with reported performance of AI tools matching or in some …

Objectives To assume the ideal cut-off values and diagnostic performance of two thyroid
imaging reporting and data systems published by the Korean Thyroid Association/Korean …

Context Ultrasound-based risk stratification systems (Thyroid Imaging Reporting and Data
Systems (TIRADSs)) of thyroid nodules (TNs) have been implemented in clinical practice …

Background The performance in evaluating thyroid nodules on ultrasound varies across
different risk stratification systems, leading to inconsistency and uncertainty regarding …

Objective To determine if the artificial intelligence‐based Thyroid Imaging, Reporting and
Data System (AI TIRADS) would perform better than the American College of Radiology …

Objective The most commonly employed diagnostic criteria for identifying thyroid nodules
include Thyroid Imaging and Reporting Data System (TI‐RADS) and American Thyroid …

Objective The incidence of thyroid cancer has significantly increased in recent decades.
Although most thyroid cancers are small and carry an excellent prognosis, a subset of …

Objective: To determine the sensitivity of ACR-TIRADS scoring by comparing its results with
those obtained on biopsy of respective specimens. Method: This prospective study including …

The studies on the effectiveness of various TIRADS in the diagnostics of thyroid nodules
differ in the method of ultrasound image assessment: real time (rtUS) vs. static …

Background Fine-needle aspiration biopsy (FNAB) is a good diagnostic tool for thyroid
nodules; however, its high false-negative rate for giant nodules remains controversial. Many …