人工智能,弹性成像和甲状腺成像报告和数据系统在可疑结节的差异诊断中的比较
Peng Cong1, Xue-Mei Wang1, Yun-Fei Zhang1
1Department of Ultrasound, The First Hospital of China Medical University, Shenyang, China.
Quantitative imaging in medicine and surgery
|January 15, 2024
概括
对于可疑的甲状腺结节,S-Detect显示出比ACR-TIRADS更高的灵敏度,但比ACR-TIRADS更低的特异性. 综合方法没有显著改善诊断性能,表明S-Detect不能取代手动诊断.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 超声波是检测甲状腺结节的主要工具.
- 评估ACR-TIRADS,S-Detect和可疑甲状腺结节的弹性图形的诊断效果.
- 评估AI和弹性图在甲状腺结节诊断中的互补作用.
研究的目的:
- 为了比较ACR-TIRADS,S-Detect和动脉弹性图的诊断性能,以检测可疑的甲状腺结节.
- 确定结合这些诊断方法的附加值.
- 评估人工智能和弹性图形在甲状腺结节评估中的实用性.
主要方法:
- 对101名患有138个甲状腺结节的患者进行了回顾性研究 (2021年1月 - 2021年11月).
- 使用ACR-TIRADS类别 (TR3-TR5) 分类的结节.
- 通过ACR-TIRADS,S-Detect和弹性图进行评估,与手术或细胞病理学结果进行比较.
主要成果:
- 与ACR-TIRADS (84.6%) 相比,S-Detect的灵敏度 (98.1%) 显著更高,但特异性较低 (19.0%vs. 40.5%).
- 在S-Detect (62.8%) 和ACR-TIRADS (64.9%) 之间的精度没有显著差异.
- 动脉弹性图 (ECI) 没有明确有用 (P=0.592);结合方法显示非显著的改善.
结论:
- 尽管S-Detect的灵敏度更高,但它不能替代可疑甲状腺结节的手动诊断.
- 对于这些结节,动脉弹性图的诊断价值仍然不确定.
- 需要进一步的研究,以澄清AI和弹性图在甲状腺结节评估中的作用.
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