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Multi-Agent Reasoning for Cardiovascular Imaging Phenotype Analysis

Created by
  • Haebom

Author

Weitong Zhang, Mengyun Qiao, Chengqi Zang, Steven Niederer, Paul M Matthews, Wenjia Bai, Bernhard Kainz

Outline

In this paper, we present MESHAgents, a large-scale language model (LLM)-based multi-agent framework for cardiovascular disease imaging analysis. MESHAgents automatically identifies complex nonlinear dependencies between imaging phenotypes and disease risk factors and outcomes by leveraging multidisciplinary AI agents from cardiology, biomechanics, statistics, and clinical research. This is an attempt to overcome the limitations of existing human-centered hypothesis testing and correlated factor selection methods, and the performance of the system is validated through a population-based study using cardiac and aortic imaging phenotypes. MESHAgents identifies additional confounders beyond standard demographic factors, performs similarly to expert-selected phenotypes, and improves recall for specific disease types. It provides a scalable alternative to expert-driven approaches.

Takeaways, Limitations

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Takeaways:
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Suggesting the possibility of improving the efficiency and accuracy of cardiovascular disease image analysis by utilizing LLM-based multi-agent system.
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We present a new approach that overcomes the limitations of existing human-centered analysis methods and automatically identifies complex nonlinear relationships.
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Achieve performance similar to expert-selected phenotypes, while improving reproducibility in specific disease types.
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Providing an automated pipeline for PheWAS studies.
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Providing clinically relevant imaging phenotypes through a transparent inference process.
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Limitations:
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Further research is needed on the reliability and interpretability of the LLM.
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Generalization performance needs to be verified for various disease types and datasets.
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Lack of detailed description of interactions and decision-making processes between agents.
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Performance improvements for certain disease types are limited.
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The AUC difference is minimal (-0.004).
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