August 7, 2025

Towards Real-Time Detection of Fatty Liver Disease in Ultrasound Imaging: Challenges and Opportunities

Key Points

CORE FINDING: The AI framework achieved 98.9% accuracy in detecting fatty liver disease using ultrasound imaging.
KEY EVIDENCE: It outperformed baseline CNNs by 12.4%, improving sensitivity for advanced fibrosis and subtle features.
APPROACH: The framework integrates CNNs with machine learning classifiers and GAN-based augmentation on 10,352 images.
SIGNIFICANCE: This method addresses operator dependency and class imbalance, enhancing diagnosis in clinical settings.

Abstract

This study presents an AI framework for real-time NAFLD detection using ultrasound imaging, addressing operator dependency, imaging variability, and class imbalance. It integrates CNNs with machine learning classifiers and applies preprocessing techniques, including normalization and GAN-based augmentation, to enhance prediction for underrepresented disease stages. Grad-CAM provides visual explanations to support clinical interpretation. Trained on 10,352 annotated images from multiple Saudi centers, the framework achieved 98.9% accuracy and an AUC of 0.99, outperforming baseline CNNs by 12.4% and improving sensitivity for advanced fibrosis and subtle features. Future work will extend multi-class classification, validate performance across settings, and integrate with clinical systems.

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