Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis

作者：Huan Zhang, Xiang-Yu Wu, Wenjing Ni, Jiali Wu, Sisi Zhou, Jia Li, Ming Jin, Sitian Zhao, Zhenyao Jiang, Chao Wu, Yuxiang Sun, Junping Shi, Jie Li · 发表于：Journal of Advanced Research · 年份：2025 · DOI：10.1016/j.jare.2025.08.042 · 被引用次数：10 · 研究领域：Liver Disease Diagnosis and Treatment、Artificial Intelligence in Healthcare、Hepatitis C virus research

INTRODUCTION: Metabolic dysfunction-associated steatotic liver disease (MASLD) can progress to metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis, contributing to a heavier global health burden. Non-invasive diagnostic tools developed using machine learning (ML) and deep learning (DL), two representative artificial intelligence algorithms, are increasingly being explored for MASH and its related fibrosis assessment. OBJECTIVES: This study aimed to compare the diagnostic performance of different ML and DL models and identify the top-performing models for diagnosing MASH and associated liver fibrosis. METHODS: A systematic review and meta-analysis were conducted across PubMed, Web of Science, Embase and Cochrane Library from inception to May 18, 2025. Pooled area under the receiver operator characteristic curve (AUROC) values with 95 % confidence interval (CI) were calculated. Accuracy, specificity, sensitivity, positive predictive values, and negative predictive values were also recorded. RESULTS: Of 4,314 studies initially identified, 106 met the inclusion criteria, with 35 studies (ML: n = 28; DL: n = 7) providing data for analysis. Logistic Regression and Neural Network are the most commonly algorithms applied in ML and DL, respectively. The pooled AUROCs for diagnosing MASH were 0.833 (95 %CI: 0.806-0.860) for ML models and 0.841 (95 %CI: 0.782-0.900) for DL models. Light Gradient Boosting Machine (LightGBM) and ResNet50 were the best-performing mod...