Meta-Analysis of AI Integration in Abdominal Imaging for Liver Fibrosis and MASLD: Evaluating Diagnostic Accuracy and Clinical Impact

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Abstract
Background/Objectives: To evaluate the diagnostic accuracy of artificial intelligence (AI)-based imaging techniques for liver fibrosis and metabolic dysfunction-associated steatotic liver disease (MASLD). Materials and Methods: We performed a comprehensive search in PubMed, Embase, Cochrane Library, and Web of Science until August 2025. A total of 15 studies (mean age of patients 56 years, 60% male) were included. The risk of bias in the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Diagnostic performance metrics were calculated using a random-effects bivariate model, including the area under the curve (AUC), sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio. Meta-regression analysis was conducted to investigate potential sources of heterogeneity when I2 was ≥50%. A p-value < 0.05 was considered statistically significant. Results: For liver fibrosis, pooled sensitivity was 0.85, specificity was 0.81, and AUC was 0.92. For MASLD, sensitivity was 0.86, specificity was 0.95, and AUC was 0.99. Different imaging modalities and AI classifiers caused significant study heterogeneity. To avoid misleading pooled estimates across varied datasets, imaging modality and AI model subgroup analyses were performed. Only three studies were used to estimate MASLD; therefore, considerable between-study heterogeneity should be considered. Conclusions: AI-based imaging modalities demonstrate promising diagnostic accuracy for liver fibrosis and MASLD, warranting further standardization to enhance diagnostic consistency.

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