Fatty liver disease is a common liver condition that, traditionally, has been assessed by clinicians based on subjective imaging impressions—such as the ‘bright liver’ appearance on ultrasound—or general liver function tests, making it difficult to accurately quantify the degree of hepatic steatosis. In recent years, however, advances in medical technology have moved fatty liver grading beyond reliance on experience alone. Novel non-invasive tools—such as transient elastography (e.g., FibroScan) with Controlled Attenuation Parameter (CAP) and Magnetic Resonance Imaging–Proton Density Fat Fraction (MRI-PDFF)—now enable objective, quantitative measurement of liver fat content, allowing precise classification into mild, moderate, or severe stages. This not only facilitates early detection and timely intervention but also supports dynamic monitoring of treatment response, significantly improving diagnostic accuracy and patient management. Furthermore, artificial intelligence–assisted image analysis is continuously enhancing the automated detection and grading of fatty liver. Looking ahead, diagnosis will become increasingly standardized and personalized, offering robust support for chronic disease prevention and control.
脂肪肝是一种常见的肝脏疾病，传统上医生多依赖影像学经验（如超声波的“亮肝”表现）或肝功能指标进行粗略判断，难以精准量化脂肪变性程度。近年来，随着医学技术的发展，脂肪肝的分级已逐步告别‘凭经验’的时代。新型无创检测手段，如瞬时弹性成像（FibroScan）结合受控衰减参数（CAP）、磁共振质子密度脂肪分数（MRI-PDFF）等，能够客观、定量地评估肝脏脂肪含量，实现从轻度、中度到重度的精准分级。这不仅有助于早期发现和干预，还能动态监测治疗效果，显著提升诊疗效率与患者管理质量。此外，人工智能辅助影像分析也在不断优化脂肪肝的自动识别与分级能力。未来，脂肪肝的诊断将更加标准化、个体化，为慢病防控提供有力支持。
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