Enhancing Nonalcoholic Fatty Liver Disease (NAFLD) Detection: AI-Enhanced Ultrasound Validation and Performance Improvement

Nonalcoholic Fatty Liver Disease (NAFLD) is one of the most common chronic liver diseases worldwide and is strongly associated with obesity, type 2 diabetes mellitus, metabolic syndrome, and other cardiovascular risk factors. If left undiagnosed, NAFLD may progress to nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma, making early detection essential for effective clinical management. Although magnetic resonance-based fat quantification is regarded as the noninvasive reference standard for assessing hepatic steatosis, its high cost, limited accessibility, and lengthy examination time restrict its widespread use, particularly in low-resource healthcare settings. Ultrasound imaging provides an affordable, safe, and widely available alternative; however, its diagnostic accuracy is often affected by operator dependency and subjective interpretation. This study investigates the feasibility of integrating artificial intelligence (AI) with ultrasound imaging to improve the accuracy and reliability of NAFLD detection. The proposed framework employs advanced deep learning algorithms, including convolutional neural networks (CNNs) and transfer learning, to automatically extract and classify discriminative imaging features associated with hepatic fat accumulation. To enhance model robustness and generalization, the training dataset was expanded to include a larger and more diverse patient population. Furthermore, multimodal data comprising ultrasound images, demographic information, clinical history, and laboratory findings were integrated to improve diagnostic performance and support more informed clinical decision-making. Model performance was assessed using accuracy, precision, recall, F1-score, receiver operating characteristic–area under the curve (ROC-AUC), k-fold cross-validation, and external validation to ensure reliability and clinical applicability. The findings indicate that AI-enhanced ultrasound can significantly improve diagnostic consistency, reduce observer variability, and provide a cost-effective, noninvasive solution for routine NAFLD screening and early detection. The proposed approach demonstrates strong potential for supporting clinical decision-making and improving patient outcomes, particularly in resource-constrained healthcare environments where access to advanced imaging modalities remains limited.

Keywords: Nonalcoholic Fatty Liver Disease (NAFLD), Artificial Intelligence, Ultrasound Imaging, Deep Learning, Convolutional Neural Networks, Transfer Learning, Multimodal Data, Early Detection.