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深度学习方法来区分甲状腺结节与化:一项两中心研究.

Chen Chen1,2,3, Yuanzhen Liu1,2,3, Jincao Yao1,4,5

  • 1Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.

BMC cancer
|November 23, 2023
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概括

深度学习模型在识别恶性甲状腺化结节方面明显优于放射科医生. 这些人工智能工具还可以提高临床医生使用时的诊断准确性.

关键词:
化是一种化过程.深度学习是一种深度学习.甲状腺结节 甲状腺结节超声波学 超声波学 超声波学

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 在瘤学瘤学.

背景情况:

  • 甲状腺结节经常表现出化,但其诊断意义尚未完全理解.
  • 区分良性和恶性甲状腺结需要客观的评估方法.

研究的目的:

  • 开发和评估深度学习 (DL) 模型,用于客观区分良性和恶性甲状腺结.
  • 将DL模型的诊断性能与经验丰富的放射科医生的诊断性能进行比较.

主要方法:

  • 从两个中心对631个病理确认的甲状腺结节进行了回顾性分析.
  • 利用超声波图像数据集进行深度学习模型培训和验证.
  • 评估的诊断性能使用接收机-运营商特征曲线 (AUROC) 下的区域.

主要成果:

  • Xception DL模型实现了最高的AUROC (0.970),其次是DenseNet169 (0.959).
  • 与放射科医生相比,这两种DL模型都表现出优越的诊断性能 (P < 0.05).
  • Xception的有效性与其使用深度可分离卷积来有效提取特征有关.

结论:

  • 深度学习模型在分类化甲状腺结节方面明显优于放射科医生.
  • 基于DL的工具有可能提高放射科医生对甲状腺结节的诊断能力.