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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
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甲状腺瘤分类的年龄分层深度学习模型:一个多中心诊断研究.

Weijie Zou1,2,3,4, Yahan Zhou2,5, Jincao Yao1,2,3,4

  • 1Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, China.

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概括

与非分层模型和放射学家相比,一个年龄分层的深度学习模型 (ASMCNet) 显著提高了甲状腺结节分类的准确性. 这种人工智能工具提高了诊断性能,有助于减少对甲状腺癌的不必要活检.

关键词:
年龄年龄年龄年龄年龄年龄人工智能的人工智能是人工智能.深度学习是一种深度学习.甲状腺癌是什么?甲状腺癌是什么?超声波学 超声波学 超声波学

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科学领域:

  • 人工智能在医学中的应用
  • 医学成像分析 医学成像分析
  • 瘤学 诊断 诊断 瘤学

背景情况:

  • 甲状腺癌的发病率正在上升,而年龄是关键的生存预测因素.
  • 由于死亡率低,目前的诊断方法可能导致过度诊断和过度治疗.
  • 甲状腺结节分类中年龄的诊断影响需要进一步调查.

研究的目的:

  • 开发一个年龄分层的深度学习 (DL) 模型,ASMCNet,用于甲状腺结节的分类.
  • 评估年龄分层对DL模型准确性的影响.
  • 探索ASMCNet在提高放射科医生的诊断性能和减少不必要的活检方面的潜力.

主要方法:

  • 对来自三个医院的5934名患者的10,391张超声波图像进行了回顾性分析.
  • 开发和验证一个年龄分层的深度学习模型 (ASMCNet).
  • 与使用DeLong测试的非年龄分层模型和放射科医生相比,ASMCNet的性能比较.

主要成果:

  • 在ASMCNet中,AUROC为0.906,灵敏度为86.1%,特异性为85.1%.
  • ASMCNet显著优于非年龄分层模型 (AUROC 0.867) 和所有放射科医生.
  • 放射科医生的表现在人工智能协助下得到改善,特别是使用解释热图.

结论:

  • 在DL模型中的年龄分层显著提高了甲状腺瘤分类的准确性.
  • ASMCNet证明了临床适用性,协助放射科医生提高诊断准确度.
  • 年龄分层的方法对于准确的甲状腺结节诊断至关重要,可能减少过度治疗.