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代谢相关脂肪性肝病的智能诊疗与数字健康综合管理
DOI: 10.12449/JCH260422
利益冲突声明：本文不存在任何利益冲突。
作者贡献声明：蒋晔玮负责设计论文框架，起草论文，修改论文；徐蕴怡、苟明阳、乔王宇负责实施研究过程，起草论文；何钰茹负责数据收集，绘制图表；周婧琪负责拟定写作思路，指导撰写文章并最后定稿。
Intelligent diagnosis and treatment and comprehensive digital health management of metabolic dysfunction-associated fatty liver disease
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摘要: 代谢相关脂肪性肝病（MAFLD）已成为全球范围内最常见的慢性肝病之一，构成严峻的公共卫生挑战。在此背景下，融合人工智能技术，特别是机器学习的智能诊疗和数字健康干预，能够突破传统方法局限，高效筛选关键基因、生物标志物、生化代谢等多维度数据，实现MAFLD风险预测、亚型识别、疗效评估等革命性突破。本文系统综述了机器学习模型在驱动MAFLD临床诊断革新与精准风险预测中的突破性应用；全面比较并分析了国内外MAFLD数字健康实践案例，深入剖析其在研究对象、干预方式及管理团队等方面的优势与局限。研究表明，数字健康与MAFLD长期管理的深度整合，正成为推动疾病管理模式向智能化、个体化、精准化转型的核心动力，但也存在诸多伦理技术问题亟待解决。Abstract: Metabolic dysfunction-associated fatty liver disease （MAFLD） has become one of the most prevalent chronic liver diseases worldwide， posing a serious challenge to public health. In this context， the integration of artificial intelligence （AI）， especially intelligent diagnosis and treatment and digital health interventions based on machine learning， can break through the limitations of traditional methods， realize efficient screening of multi-dimensional data such as key genes， biomarkers， and biochemical metabolites， and achieve revolutionary breakthroughs in risk prediction， subtype identification， and therapeutic effect assessment for MAFLD. This article systematically reviews the ground-breaking application of machine learning models in driving the innovation of clinical diagnosis and precise risk prediction of MAFLD， conducts a comprehensive comparative analysis of digital health practice cases of MAFLD in China and globally， and deeply analyzes their advantages and limitations in terms of research subjects， interventions， and management team. Studies have shown that the deep integration of digital health and long-term management of MAFLD is becoming the key engine driving the transformation of disease management modes towards an intelligent， individualized， and precise era， but there are various ethical and technical issues that need to be addressed urgently.
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表 1 现有MAFLD数字健康研究的监测指标对比
Table 1. A comparison and collation of the monitoring indicators for current digital health research on MAFLD
指标类型 代表指标 优势 局限性 人体测量学 体重、BMI、腰围 简便、低成本 精度有限，标准化不足 身体成分分析 肌肉量、内脏脂肪［35，47，49］ 精准评估肥胖与少肌症 设备要求高，不易普及 临床评估 病史［35，39，41，49］、血压 识别心血管风险 回忆偏倚，瞬时因素干扰 血液生化 肝酶、代谢指标［40，42，46］、炎症
［44］、凝血［30，35，37］全面反映代谢与炎症状态 部分新型标志物（如PRO-
C3［44］、CK-18［35］）成本高，未经
临床验证影像学 MRI-PDFF［35，47-48］、超声［28］、瞬时弹
性成像［34，44］、CAP［26，34，47］、肝硬度
测量［42，45-47］MRI-PDFF是肝脂肪变性的无创
定量金标准；新兴技术可实现床
旁快速检测超声灵敏度有限，MRI成本高；
新兴技术的准确度难以保证组织学 肝活检［35］ 诊断精准 有创、成本高，且临床难以推广 风险评分 FLI［44，49，51］、FIB-4指数［35，37，45，47］、
APRI［44］多指标整合，无创评估 部分风险评分对晚期肝纤维化
识别性能不足行为与功能 体力活动、睡眠［35］、心理量表［34，43］、
心肺功能［35，48］、疼痛评估［41，47］贴近干预目标，主观与客观相结合 部分量表与评估缺乏统一标准 注：MAFLD，代谢相关脂肪性肝病；BMI，体重指数；PRO-C3，人Ⅲ型胶原前肽；CK-18，细胞角蛋白18；MRI-PDFF，磁共振质子密度脂肪分数；CAP，受控衰减参数；FLI，肝脂肪指数；APRI，天冬氨酸转氨酶与血小板比值指数。
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