贝塞斯达类别IV甲状腺结节的AI诊断
Jincao Yao1,2,3,4, Yanming Zhang5,6, Jiafei Shen1,2
1Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China.
iScience
|October 23, 2023
概括
人工智能 (AI) 现在可以改善未确定的甲状腺结节的诊断. 分析超声波图像的AI模型在分类贝塞斯达IV甲状腺结节方面表现出高准确性,有助于治疗决策.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 甲状腺结节很常见,精细针吸收细胞学 (FNAC) 是主要的诊断工具.
- FNAC对卵泡甲状腺结节有限制,将其归类为贝塞斯达IV,这阻碍了精确的诊断和治疗计划.
研究的目的:
- 开发和评估人工智能 (AI) 模型,以使用超声波 (US) 成像来改进贝塞斯达IV甲状腺结节的分类.
- 评估人工智能的潜力,以完善不确定的甲状腺结节的诊断和管理.
主要方法:
- 一项回顾性研究利用贝塞斯达IV甲状腺结节的超声波图像数据,这些结节在2006年至2022年期间从五家医院收集.
- 培训和测试美国基于图像的AI模型,以识别Bethesda IV甲状腺结节中的特定类别.
主要成果:
- 在独立的数据集上,表现最好的AI模型在曲线下的面积 (AUC) 达到0.90至0.95.
- 人工智能模型在诊断贝塞斯达IV甲状腺结节方面显示出临床应用的巨大潜力.
结论:
- 超声波图像的AI驱动分析为贝塞斯达IV甲状腺结节的诊断提供了有希望的进步.
- 这项技术有可能显著改变目前对未确定性甲状腺结节的诊断和管理范式.
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