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Updated: May 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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基于CT的人工智能系统补充了深度学习模型和肝纤维化阶段化放射科医生.

Shuang Zheng1, Wenao Ma2, Lin Mu1

  • 1Department of Radiology, The First Hospital of Jilin University, Changchun, China.

iScience
|April 18, 2025
PubMed
概括

一个新的深度学习模型 (Model-C) 准确地非侵入性地分阶段肝纤维化. 整合人工智能和放射科医生的补充系统 (DRCDS) 实现了高诊断准确性,提高了个人表现.

关键词:
人工智能的人工智能是人工智能.病理生理学 病理生理学公共卫生 公共卫生

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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
  • 肝病学 肝病学是一种肝病学.

背景情况:

  • 肝纤维化阶段测定对于预测患者发病率和死亡率至关重要.
  • 对于肝纤维化病阶段的非侵入性方法有很高的需求.
  • 目前的方法在准确性和通用性方面面临挑战.

研究的目的:

  • 开发一种自动化的深度学习 (DL) 模型,用于非侵入性肝纤维化病阶段.
  • 创建一个深度学习-放射科医生互补决策系统 (DRCDS),以提高诊断准确度.
  • 在多重分类任务中解决模型概括和人机互补问题.

主要方法:

  • 开发了一个基于DL的自动化细分和分类模型 (Model-C).
  • 员工测试时间的调整,以减轻数据分布的转移.
  • 建立了一个DRCDS,使用AI-放射科医生合作的决策模型.

主要成果:

  • 模型C表现出高性能 (AUC为0.89-0.92),表现优于仅肝脏或仅脏的模型.
  • 测试时间的调整改善了Model-C对外部数据集的Obuchowski指数.
  • DRCDS略高于模型-C和高级放射科医生,模型-C诊断的采用率很高 (73.7%-92.0%).

结论:

  • DRCDS为诊断肝纤维化提供了一个非常准确的方法.
  • 该研究为医疗AI的模型概括和人机互补性提供了有效的解决方案.
  • 这项工作推进了非侵入性肝纤维化分期和人工智能辅助的临床决策.