人工智能用于在代谢功能障碍相关的脂肪性肝病中诊断纤维化:系统性审查
Neilson Silveira de Souza1, Théo Cordeiro Veiga Vitório1, Raphael Augusto de Souza2,3
1Faculty of Medicine of Bahia, Federal University of Bahia, Salvador 40026-010, Bahia, Brazil.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
人工智能 (AI) 显示出在代谢功能障碍相关脂肪性肝病 (MASLD) 中诊断肝纤维化的前景,其表现优于传统得分. 然而,更广泛的临床使用需要更多的外部验证和多样化的队列研究.
科学领域:
- 医疗人工智能 医疗人工智能
- 肝病学 肝病学是一种肝病学.
- 诊断技术 诊断技术 诊断技术
背景情况:
- 代谢功能障碍相关的性肝病 (MASLD) 诊断需要准确的肝纤维化评估.
- 人工智能 (AI) 是一种新兴的工具,用于在MASLD中诊断肝纤维化.
- 目前还没有对AI在MASLD中的诊断性能进行全面的综合分析.
研究的目的:
- 系统地审查和评估人工智能模型来诊断或分期MASLD患者的肝纤维化.
- 将人工智能模型的性能与 MASLD 肝纤维化常规诊断工具进行比较.
- 为了确定共同的预测特征,并分析用于MASLD纤维化诊断的AI架构.
主要方法:
- 对使用人工智能用于MASLD肝纤维化诊断的研究进行系统审查,在多个数据库中进行搜索 (PubMed,Scopus等). ) 的情况.
- 包括成人MASLD患者的初级研究,包括偏差风险评估 (QUADAS-2) 和报告评估 (MINIMAR).
- 叙述综合按数据类型 (临床/实验室与成像) 分组,包括基于频率的预测特征分析和AI架构.
主要成果:
- 分析了21项涉及19,221名患者和5237张图像的研究.
- 人工智能模型在与非侵入性得分 (如FIB-4和NFS) 相比,显示出更高的诊断性能.
- 尽管偏差风险一般较低,但发现方法透明度和外部验证有限.
结论:
- 人工智能是一种可行的方法,用于在MASLD中进行非侵入性肝纤维化诊断,超过标准临床评分的准确性.
- 由于缺乏外部验证和显著的研究异质性,广泛的临床应用受到阻碍.
- 在多样化的多中心队列中进行前性验证对于将AI整合到常规临床实践中至关重要.
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