在MASLD中基于人工智能的风险预测模型:系统审查
Basile Njei1, Yazan A Al-Ajlouni2, Samira Yaya Lemos3
1International Medicine Program (Section of Digestive Diseases), Yale School of Medicine, Yale University, New Haven, CT, USA. basile.njei@yale.edu.
Digestive diseases and sciences
|November 13, 2025
概括
人工智能模型显示出预测代谢功能障碍相关的脂肪性肝病 (MASLD) 风险和患者分层的强大潜力. 为了临床整合,需要对数据多样性和可解释性的进一步研究.
科学领域:
- 医疗信息学 医疗信息学
- 肝病学 肝病学是一种肝病学.
- 人工智能的人工智能
背景情况:
- 与代谢功能障碍相关的脂肪性肝病 (MASLD) 是一个日益严重的全球健康问题.
- 早期识别有重大疾病进展风险的患者 (例如,晚期纤维化,MASH) 对于有效管理至关重要.
- 在了解AI在MASLD风险预测和患者分层中的有效性方面存在差距.
研究的目的:
- 系统地审查和评估基于AI的模型在预测MASLD风险方面的表现.
- 评估人工智能模型根据疾病严重程度分层患者的能力,包括纤维化和MASH.
- 确定AI中用于MASLD风险评估的关键预测因素和常用方法.
主要方法:
- 根据PRISMA指南,在主要数据库中进行了全面的系统文献搜索.
- 研究是基于基于AI的MASLD风险预测的预定义标准.
- 进行了数据提取和质量评估 (QUADAS-2),该研究在PROSPERO.中注册.
主要成果:
- 包括来自不同地理区域的26项研究 (2014-2025年),其中偏差风险主要较低.
- 人工智能模型表现出强大的预测性能:MASH的AUROC在0.76-0.95之间,纤维化 (≥F2-F4) 的AUROC在0.72-0.94.4之间.
- 常见的预测因素包括年龄,BMI,肝酶和血小板;多模式数据 (临床,成像,弹性图像) 经常改善了歧视.
结论:
- 基于人工智能的模型对预测MASLD风险和分层患者显著有前途.
- 这些人工智能模型的预测能力强大,为改善临床决策提供了潜力.
- 提高数据多样性和模型可解释性是成功在MASLD中临床实施AI的关键未来步骤.
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