Multi-agent Reasoning for Cardiovascular Imaging Phenotype Analysis

Zhang W, Qiao M, Zang C, Niederer S, Matthews PM, Bai W, Kainz B (2026)

Publication Type: Conference contribution

Publication year: 2026

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 15960 LNCS

Pages Range: 429-439

Conference Proceedings Title: Lecture Notes in Computer Science

Event location: Daejeon, KOR

ISBN: 9783032049261

DOI: 10.1007/978-3-032-04927-8_41

Abstract

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 Multi-agent Exploratory Synergy for the Heart (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. MESHAgents 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±0.010 on disease classification tasks. Notably, the recall score improves for 6 out of 9 disease types. Our framework provides clinically relevant imaging phenotypes with transparent reasoning, offering a scalable alternative to expert-driven methods.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) University College London (UCL) GB United Kingdom (GB) University of Tokyo JP Japan (JP)

How to cite

APA:

Zhang, W., Qiao, M., Zang, C., Niederer, S., Matthews, P.M., Bai, W., & Kainz, B. (2026). Multi-agent Reasoning for Cardiovascular Imaging Phenotype Analysis. In James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim (Eds.), Lecture Notes in Computer Science (pp. 429-439). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.

MLA:

Zhang, Weitong, et al. "Multi-agent Reasoning for Cardiovascular Imaging Phenotype Analysis." Proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, Daejeon, KOR Ed. James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim, Springer Science and Business Media Deutschland GmbH, 2026. 429-439.

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