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基于超声波特征的甲状腺结节人工智能模型的诊断性能:系统性审查和元分析

Jianfeng Zhan^1,2, Jian Zhang³, Shaoqi Zhu^1,2

¹Department of Endocrinology, Huzhou First People's Hospital, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China.

Frontiers in oncology

|September 19, 2025

概括

人工智能 (AI) 模型在使用超声波诊断甲状腺结节时显示出高精度,EDLC-TN模型是最有效的. 在小于20毫米的结节和50岁以上的患者中,可以看到最佳的性能.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

使用超声波的人工智能 (AI) 诊断模型越来越多地用于甲状腺结节评估.
不同的人工智能辅助方法的诊断性能存在显著的变化.

研究的目的:

系统地评估基于超声波的AI模型,以区分良性和恶性甲状腺结节.
为了确定甲状腺结节的最有效的AI诊断模型.

主要方法:

在PubMed,科学网和Cochrane图书馆进行了全面的文献搜索.
对28项研究 (134,028名患者) 的元分析使用标准统计软件进行.
使用QUADAS-2评估了研究质量,并进行了子组分析.

主要成果:

人工智能模型显示出高诊断性能,聚合灵敏度为0.89和特异性为0.84.
总结接收器操作特征曲线 (SROC-AUC) 下的面积为0.93.
EDLC-TN模型表现出最高的诊断准确性,在特定亚组 (例如,结节<20毫米,患者≥50岁) 中性能得到改善.

结论:

人工智能辅助的诊断技术为甲状腺结节评估提供了显著的潜力.
EDLC-TN模型显示出高临床效用,特别是在较小的结节和老年患者中.
进一步的研究可能会改进人工智能应用,以改善甲状腺结节的诊断.

关键词:

人工智能的人工智能是人工智能.诊断诊断诊断诊断诊断诊断甲状腺新生体甲状腺瘤甲状腺结节甲状腺结节超声波超声波是指超声波的使用.

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