A novel approach for liver steatosis assessment in ultrasound images using an adapted Vision-Language Foundation Model

Metabolic dysfunction–associated steatotic liver disease (MASLD) is among the most prevalent chronic liver diseases worldwide. B-mode ultrasound remains the first-line screening modality, yet its qualitative nature and inter-observer variability limit diagnostic consistency. Existing AI approaches largely rely on single-image classification with opaque outputs that do not reflect exam-level clinical reasoning. In this study, we aimed to develop a vision–language model (VLM) framework for automated, exam-level hepatic steatosis assessment from multi-image ultrasound, producing outputs aligned with the Hamaguchi scoring system. We developed a two-stage framework. First, a compact VLM (Qwen3-VL-8B-Instruct) was adapted to abdominal ultrasound using clinically grounded visual question answering on 16,232 images from 1925 patients. Second, the adapted model performed exam-level Hamaguchi scoring by jointly analyzing all ultrasound images from a given examination using a structured clinical prompt, without any additional task-specific training. Performance was evaluated on an independent cohort with liver histology specimen (179 patients, 383 images) and compared with majority-vote scoring by five expert clinicians. At the feature level, the VLM achieved 95.1% accuracy ( κ = 0.90 − 0.96 ), outperforming image-only baselines on clinically relevant tasks. At the exam level, AI-derived Hamaguchi scores showed strong correlation with histological steatosis ( ρ = 0.94 ), exceeding clinician performance ( ρ = 0.83 , p < 0.001 ). Discrimination was high across clinical steatosis severity thresholds ( AUC = 0.92 − 0.99 ), with significant improvements over clinicians at lower severity levels. The proposed compact vision–language framework demonstrated the ability to generate exam-level ultrasound assessments of hepatic steatosis that closely align with biopsy-derived histological severity. It supports scalable, auditable assessment for clinical screening and longitudinal monitoring.