多模式超声波和人工智能用于在哈西莫托甲状腺炎中描述甲状腺结节:当前的挑战和未来的前景
Siyu DuanYang1,2, Wenhao Qu1,2, Yanfang Wang2
1The Second Clinical Medical School, Lanzhou University, Lanzhou, 730030, China.
European journal of medical research
|February 20, 2026
概括
哈西莫托的甲状腺炎 (HT) 患者患甲状腺癌的风险更高. 多模超声波与AI显示出诊断HT相关甲状腺结节的前景,尽管准确性需要改进.
科学领域:
- 内分泌学 在内分泌学.
- 放射学 放射学是一门学科.
- 医疗人工智能 医疗人工智能
背景情况:
- 哈西莫托氏甲状腺炎 (HT) 患者患甲状腺 papillary carcinoma (PTC) 的风险增加.
- 在HT的腺纤维化可以产生状区域,使恶性瘤的评估复杂化.
- 在HT中区分恶性和良性结节是常规超声波的挑战.
研究的目的:
- 审查多式超声波和人工智能的进展,以诊断HT患者的良性和恶性甲状腺结节.
- 评估AI在HT相关甲状腺结节诊断中的当前状态和未来方向.
主要方法:
- 对甲状腺结节评估的多式超声波技术的审查.
- 整合人工智能 (AI) 算法与超声数据.
- 细针吸收活检 (FNAB) 作为诊断标准的分析.
主要成果:
- 多模式超声波与FNAB相结合,对HT相关的甲状腺结节的评估是有效的.
- 在诊断HT相关的甲状腺结节 (TNs) 中AI的应用需要进一步细化以提高准确性.
- 目前对HT相关的TN的AI诊断准确性需要提高.
结论:
- 多模式超声波为评估HT相关甲状腺结节提供了一种经济有效和可重复的方法.
- 人工智能集成具有提高诊断准确性的潜力,但需要进一步开发.
- 对人工智能增强的多式超声波的持续研究对于更好的HT患者护理至关重要.
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