Multi-Agent Reasoning for Cardiovascular Imaging Phenotype Analysis

Identifying the associations between imaging phenotypes and disease risk factors and outcomes is essential for understanding disease mechanisms and improving diagnosis and prognosis models. However, traditional approaches rely on human-driven hypothesis testing and selection of association factors, often overlooking complex, non-linear dependencies among imaging phenotypes and other multi-modal data. To address this, we introduce a MESHAgents framework that leverages large language models as agents to dynamically elicit, surface, and decide confounders and phenotypes in association studies, using cardiovascular imaging as a proof of concept. Specifically, we orchestrate a multi-disciplinary team of AI agents, which spontaneously generate and converge on insights through iterative, self-organizing reasoning. The framework dynamically synthesizes statistical correlations with multi-expert consensus, providing an automated pipeline for phenome-wide association studies (PheWAS). We demonstrate the system’s capabilities through a population-based study of imaging phenotypes of the heart and aorta. The framework autonomously uncovered correlations between imaging phenotypes and a wide range of non-imaging factors, identifying additional confounder variables beyond standard demographic factors. Validation on diagnosis tasks reveals that MESHAgents-discovered phenotypes achieve performance comparable to expert-selected phenotypes, with mean AUC differences as small as -0.004 on disease classification tasks. Notably, the recall score based on MESHAgents improves for 6 out of 9 disease types. These results demonstrate MESHAgents’ ability to automatically discover clinically relevant imaging phenotypes with transparent reasoning trails, providing a scalable alternative to traditional expert-driven approaches in medical imaging studies.