用AI非侵入式诊断脂肪肝并分阶段，准确率超86%。
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
- 构建级联AI模型融合人体与检验数据，实现无创诊断。
- 对脂肪肝分期准确率达86%，区分NASH与非NASH的AUC达96%。
- 适合临床早期筛查，助力降低肝病医疗负担。
非酒精性脂肪肝病（NAFLD）是全球最普遍的肝脏疾病之一，可能进展为非酒精性脂肪性肝炎（NASH）、肝纤维化、肝硬化及肝细胞癌。由于症状不特异且肝活检具有侵入性，诊断与分期面临挑战。本研究提出一种基于集成学习与特征融合的新型人工智能级联模型，开发出一种非侵入性、鲁棒且可靠的诊断工具，利用体征与实验室参数实现NAFLD的早期检测与干预。该模型在NASH脂肪变性分期任务中达到86%的准确率（非NASH、脂肪变性1级、2级、3级），在区分NASH（1-3级脂肪变性）与非NASH病例时，AUC-ROC高达96%，优于现有先进模型。这一显著性能提升凸显了人工智能在NAFLD早期诊断与治疗中的应用潜力，有助于改善患者预后，减轻晚期肝病带来的医疗负担。
原文摘要 · Abstract (English)
Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. Diagnosing and staging NAFLD presents challenges due to its non-specific symptoms and the invasive nature of liver biopsies. Our research introduces a novel artificial intelligence cascade model employing ensemble learning and feature fusion techniques. We developed a non-invasive, robust, and reliable diagnostic artificial intelligence tool that utilizes anthropometric and laboratory parameters, facilitating early detection and intervention in NAFLD progression. Our novel artificial intelligence achieved an 86% accuracy rate for the NASH steatosis staging task (non-NASH, steatosis grade 1, steatosis grade 2, and steatosis grade 3) and an impressive 96% AUC-ROC for distinguishing between NASH (steatosis grade 1, grade 2, and grade3) and non-NASH cases, outperforming current state-of-the-art models. This notable improvement in diagnostic performance underscores the potential application of artificial intelligence in the early diagnosis and treatment of NAFLD, leading to better patient outcomes and a reduced healthcare burden associated with advanced liver disease.
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