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.

翻译：非酒精性脂肪肝病（NAFLD）是全球范围内最普遍的肝脏疾病之一，其进展为更严重疾病（如非酒精性脂肪性肝炎（NASH）、肝纤维化、肝硬化及肝细胞癌）的风险极高。由于NAFLD症状非特异性且肝活检具有侵入性，其诊断与分级面临挑战。本研究提出一种采用集成学习与特征融合技术的新型人工智能级联模型。我们开发了一种基于人体测量学与实验室参数的无创、稳健且可靠的诊断人工智能工具，有助于实现NAFLD进展的早期检测与干预。该新型人工智能在NASH脂肪变性分级任务（非NASH、脂肪变性1级、2级及3级）中达到86%的准确率，并在区分NASH（脂肪变性1级、2级及3级）与非NASH病例时获得96%的AUC-ROC值，性能优于当前最先进模型。诊断性能的显著提升凸显了人工智能在NAFLD早期诊断与治疗中的应用潜力，有望改善患者预后并减轻晚期肝病相关的医疗负担。