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Introduction. Chronic diffuse liver diseases in children require safe noninvasive assessment and repeated monitoring of fibrosis. Liver biopsy remains the reference method for morphological verification; however, in pediatric practice its use is limited by invasiveness, risk of complications, and ethical considerations. Multiparametric ultrasound, including shear wave elastography and Doppler assessment of blood flow, is suitable for longitudinal follow-up, yet interpretation of ultrasound features remains operator-dependent. Automated image analysis using artificial intelligence algorithms may increase objectivity and reproducibility of fibrosis stratification and support clinical decision-making. Aim. To evaluate the diagnostic accuracy of multiparametric ultrasound, including two-dimensional shear wave elastography and machine learning algorithms, for staging liver fibrosis in children. Materials and methods: This retrospective single-center study included 185 children (140 with chronic diffuse liver diseases and 45 controls). All participants underwent two-dimensional shear wave elastography, hepatic vein Doppler assessment, and grayscale ultrasound image analysis using two machine learning approaches: Model A (random forest algorithm; binary classification) and Model B (hierarchical ensemble; multiclass classification). The reference approach was clinical verification and risk stratification based on elastography findings. Results. A strong positive association was observed between elastography measures and Model B predictions (Spearman rank correlation coefficient 0.77). Model A demonstrated 84% specificity in differentiating normal findings from pathological changes. Model B showed higher sensitivity (83%) but tended to overestimate fibrosis at the boundary between minimal and moderate stages. Doppler patterns, particularly a monophasic hepatic vein waveform, were significantly associated with higher fibrosis stages. Conclusions. Integrating machine learning algorithms into the ultrasound protocol increases diagnostic objectivity; however, hierarchical approaches require calibration to reduce false-positive results in the borderline zone of early fibrosis stages.