ArticleiLIVER2025
AI-driven multimodal fusion of tongue images and clinical indicators for identifying MAFLD patients at risk of coronary artery disease: An exploratory study.
Article in iLIVER, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Article
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aims: Metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with coronary artery disease (CAD), but existing risk assessment tools lack precision and scalability. We developed an AI-driven multimodal framework integrating traditional Chinese medicine (TCM) tongue-based diagnosis with clinical biomarkers to stratify CAD risk in MAFLD. Methods: In this cross-sectional study with prospective data collection, which comprised 1073 MAFLD patients stratified by CAD status (MAFLD without CAD, Results: The coexistence of CAD was associated with significantly higher rates of hypertension, diabetes, and familial cardiovascular history in patients with MAFLD ( Conclusion: Our AI-driven dual-model framework addresses the critical unmet need for CAD identification in MAFLD patients, providing a community-scalable screening tool (Model-1) and a precision clinical assessment model (Model-2), while offering empirical support for TCM tongue diagnosis. Pending external validation to confirm its generalizability, this approach may serve as a cost-efficient non-invasive screening strategy for this high-risk population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.