Journal
AMERICAN JOURNAL OF ROENTGENOLOGY
Volume 219, Issue 4, Pages 547-554Publisher
AMER ROENTGEN RAY SOC
DOI: 10.2214/AJR.22.27430
Keywords
deep learning; machine learning; TIRADS
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This article reviews FDA-approved products for evaluating thyroid nodules on ultrasound, focusing on product features, reported performance, and implementation considerations. The products primarily perform risk stratification using Thyroid Imaging Reporting and Data System (TIRADS), with additional prediction tools independent of TIRADS.
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 instances surpassing radiologists' performance. As these data have accumulated, products for classification and risk stratification of thyroid nodules on ultrasound have become commercially available. This article reviews FDA-approved products currently on the market, with a focus on product features, reported performance, and considerations for implementation. The products perform risk stratification primarily using a Thyroid Imaging Reporting and Data System (TIRADS), though may provide additional prediction tools independent of TIRADS. Key issues in implementation include integration with radiologist interpretation, impact on workflow and efficiency, and performance monitoring. AI applications beyond nodule classification, including report construction and incidental findings follow-up, are also described. Anticipated future directions of research and development in AI tools for thyroid nodules are highlighted.
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