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Updated: Jun 10, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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可以避免的活检? 在不确定的甲状腺结节中验证基于人工智能的决策支持软件.

Christopher J Carnabatu1, David T Fetzer2, Alexander Tessnow3

  • 1Division of Endocrine Surgery, UT Southwestern Medical Center, Dallas, TX. Electronic address: https://twitter.com/CarnabatuMD.

Surgery
|October 13, 2024
PubMed
概括
此摘要是机器生成的。

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这项研究验证了用于甲状腺结节风险分层的人工智能 (AI) 系统. 人工智能提高了恶性瘤分类的准确性,可能减少对未确定的甲状腺结节进行不必要的活检.

科学领域:

  • 医学成像医学成像
  • 医学中的人工智能
  • 在瘤学瘤学.

背景情况:

  • 人工智能 (AI) 系统越来越多地被批准用于使用超声波数据进行甲状腺结节风险分层.
  • 验证像Koios DS这样的AI工具对于提高诊断准确性至关重要.

研究的目的:

  • 验证Koios DS人工智能 (AI) 系统,用于提高不确定性甲状腺结节的风险分层.
  • 评估AI对恶性瘤分类和活检决定的影响.

主要方法:

  • 对28个未确定的甲状腺结节进行了回顾性分析,并进行了分子测试和手术病理学.
  • 使用Koios DS评估结节,比较AI衍生的和放射学家衍生的甲状腺成像报告和数据系统 (TI-RADS) 的水平.
  • 使用AI-adapter评估恶性瘤分类性能和活检值调整.

主要成果:

  • 人工智能系统显示了与放射科医生衍生的TI-RADS水平相当一致 (科恩的 κ=0.25).
  • 人工智能适配器改善了恶性瘤的分类,将正预测值 (PPV) 从33.3%提高到54.5%,同时对负预测值 (NPV) 产生最小的影响.
  • 使用人工智能适配器可以推迟14次活检,其中13次是良性的.

结论:

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  • 科伊斯DS显示与放射科医生对未确定的甲状腺结节的评估一致.
  • 人工智能适配器增强了恶性瘤风险分层,改善了PPV,减少了NPV的最小值.
  • 人工智能辅助的风险分层可能会改善患者咨询,并减少对未确定的甲状腺结节进行不必要的活检.