A MULTIMODAL DEEP LEARNING FRAMEWORK FOR IMPROVING THE CLINICAL RELIABILITY OF ULTRASOUND-BASED LIVER STEATOSIS ASSESSMENT

Abstract

Objectives: This study aims to examine the limitations of existing deep learning approaches in ultrasound-based liver steatosis evaluation and to develop a framework that enhances clinical reliability and real-world applicability. Methodology: A structured pipeline is proposed that integrates diverse data acquisition, standardized preprocessing, and deep learning model development using convolutional architectures with transfer learning and attention mechanisms. The approach incorporates robust training strategies, external validation across heterogeneous datasets, and explainability techniques to support transparent decision-making. Findings: The results suggest that improving dataset diversity, enforcing rigorous validation protocols, and incorporating interpretability significantly enhance model stability and consistency across varying imaging conditions. These factors contribute to more dependable predictions compared to conventional performance-focused models. Novelty: This work introduces a reliability-centered perspective by embedding robustness, generalization, and explainability within a unified framework. It shifts the focus from accuracy alone toward building trustworthy systems suitable for clinical deployment, thereby narrowing the gap between experimental research and practical healthcare implementation.