深度学习用于计算机断层扫描肝纤维化和肝硬化评估:系统性审查
Numan Kutaiba1,2, Ariel Dahan1, Mark Goodwin1,2
1Department of Radiology, Austin Health, Heidelberg VIC, Australia.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
深度学习人工智能在使用计算机断层扫描 (CT) 扫描诊断肝纤维化和肝硬化方面显示出潜力. 然而,不同准确度和研究方法限制了当前的概括性.
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:
- 肝纤维化和肝硬化评估对于患者管理至关重要.
- 计算机断层扫描 (CT) 成像是一种常见的诊断工具.
- 深度学习 (DL) 为医疗图像提供了新的分析能力.
研究的目的:
- 系统地审查深度学习算法的准确性,以使用CT图像诊断肝纤维化和肝硬化.
- 评估DL模型在分类纤维化阶段中的性能.
主要方法:
- 在Medline,Embase,Web of Science和IEEE Xplore (2000-2022) 上进行系统的文献搜索.
- 包括6项回顾性研究,使用DL算法分析CT图像.
- 基于诊断准确度指标 (曲线下的面积) 评估算法.
主要成果:
- 深度学习算法在诊断显著和高级纤维化/肝硬化时表现出可变的准确性 (AUC 0.63-0.97).
- 四项研究使用图像分类,两项研究使用图像细分进行体积分析.
- 在包含的研究的子集中进行了内部和外部验证.
结论:
- 深度学习算法显示了通过CT成像对肝纤维化阶段的分类的前景.
- 目前,研究群体和方法的异质性限制了研究结果的概括性.
- 需要对标准化方法进行进一步的研究,以提高临床适用性.
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