人工智能和弹性图在甲状腺结节的诊断工作中:系统性审查和元分析
Xi Li1, Wenhan Hu1, Yong Tang2,3,4
1School of Medicine, North Sichuan Medical College, Nanchong, China.
Quantitative imaging in medicine and surgery
|March 12, 2026
概括
结合人工智能 (AI) 和弹性图学,显著提高了诊断甲状腺结节的准确性. 这种人工智能增强的弹性图形方法为甲状腺疾病检测提供了卓越的性能.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 诊断的准确性 诊断的准确性
背景情况:
- 甲状腺结节需要准确地区分良性和恶性病例.
- 人工智能 (AI) 分析超声波特征,而弹性图形测量组织度.
- 结合人工智能和弹性图学,旨在提高甲状腺结节的诊断能力.
研究的目的:
- 系统地评估结合AI与弹性学用于甲状腺结节分化的诊断准确性.
- 与现有方法相比,评估这种综合方法的性能.
主要方法:
- 在主要数据库 (PubMed,Embase,Web of Science,CNKI) 进行了全面的文献搜索.
- 包括结合人工智能和甲状腺结节弹性图的诊断研究.
- 用SROC曲线,灵敏度和特异性来评估诊断测试的性能,并通过QUADAS-2进行质量评估.
主要成果:
- 分析了19项涉及4,655个甲状腺结节超声波图像的研究.
- 人工智能和弹性图的组合显示出卓越的诊断性能.
- 聚合灵敏度为0.88,特异性为0.91,DOR为73.89,AUC为0.95. 这一比率为0.95.
结论:
- 人工智能与弹性图学相结合,证明了甲状腺结节的高诊断准确性.
- 这项技术是改善甲状腺结节诊断和预后的有希望的工具.
- 预计将其纳入常规临床实践,以提高患者的护理.
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