What question did this study set out to answer?

The aim is to create a multimodal deep learning model that integrates tongue image analysis and clinical data for MAFLD screening.

April 10, 2026Open Access

Tongue Image Analysis and Clinical Data Fusion: A Novel Approach for Non-invasive Diagnosis of Metabolic Dysfunction-associated Fatty Liver Disease

Key Points

The aim is to create a multimodal deep learning model that integrates tongue image analysis and clinical data for MAFLD screening.
Developed a deep learning model using tongue image features.
Integrated quantitative tongue diagnostics with routine clinical data.
Utilized non-invasive methodologies for diagnosis.
Demonstrated the potential for non-invasive MAFLD diagnosis.
Improved screening accuracy through multimodal approaches.

Abstract

Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a predominant cause of chronic liver disease, underscoring the demand for accessible, non-invasive diagnostic tools. Tongue diagnosis in Traditional Chinese Medicine provides a distinctive perspective on systemic health, though it remains largely subjective. This study aimed to develop an interpretable multimodal deep learning model for MAFLD screening by integrating quantitative tongue image features with routine clinical data.

Bookmark

View Full Paper

Cite This Study

Lu et al. (Wed,) studied this question.

synapsesocial.com/papers/69d893896c1944d70ce047dc https://doi.org/https://doi.org/10.14218/jcth.2025.00631

Bookmark

View Full Paper