Department of Radiology, Duke University, 2301 Erwin Rd, Box 3808, Durham, NC 27710.
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.
AJR Am J Roentgenol. 2022 Oct;219(4):1-8. doi: 10.2214/AJR.22.27430. Epub 2022 Apr 6.
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.
人工智能（AI）方法在超声评估甲状腺结节方面已经在文献中得到广泛描述，报告的 AI 工具性能与放射科医生的表现相匹配，甚至在某些情况下超越了放射科医生的表现。随着这些数据的积累，用于超声甲状腺结节分类和风险分层的产品已经商业化。本文回顾了目前市场上获得 FDA 批准的产品，重点介绍了产品的特点、报告的性能以及实施的考虑因素。这些产品主要使用甲状腺影像报告和数据系统（TIRADS）进行风险分层，尽管可能提供独立于 TIRADS 的其他预测工具。实施中的关键问题包括与放射科医生的解释集成、对工作流程和效率的影响以及性能监测。此外，还描述了甲状腺结节分类以外的 AI 应用，包括报告构建和偶然发现的随访。本文还强调了人工智能工具在甲状腺结节方面的未来研究和发展方向。
