Multi-Agent Reasoning for Cardiovascular Imaging Phenotype Analysis

• URL: http://arxiv.org/abs/2507.03460v1
• Date: Fri, 04 Jul 2025 10:30:32 GMT
• Title: Multi-Agent Reasoning for Cardiovascular Imaging Phenotype Analysis
• Authors: Weitong Zhang, Mengyun Qiao, Chengqi Zang, Steven Niederer, Paul M Matthews, Wenjia Bai, Bernhard Kainz,
• Abstract summary: We develop a framework to identify associations between imaging phenotypes and disease risk factors and outcomes.<n>We orchestrate a multi-disciplinary team of AI agents, spanning cardiology, biomechanics, statistics, and clinical research.<n>We demonstrate the system's capabilities through a population-based study of imaging phenotypes of the heart and aorta.
• Score: 9.362565834558033
• License: http://creativecommons.org/licenses/by/4.0/
• 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 -- spanning cardiology, biomechanics, statistics, and clinical research -- 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 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.

Related papers

• Causal Disentanglement for Robust Long-tail Medical Image Generation [80.15257897500578]
  We propose a novel medical image generation framework, which generates independent pathological and structural features.<n>We leverage a diffusion model guided by pathological findings to model pathological features, enabling the generation of diverse counterfactual images.
  arXiv  Detail & Related papers  (2025-04-20T01:54:18Z)
• Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT [15.942849233189664]
  Alzheimer's disease is the most prevalent neurodegenerative disease. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. We propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages.
  arXiv  Detail & Related papers  (2024-01-31T19:30:04Z)
• Multimodal Pathology Image Search Between H&E Slides and Multiplexed Immunofluorescent Images [0.0]
  We present an approach for multimodal pathology image search using dynamic time warping (DTW) on Variational Autoencoder (VAE) latent space. Through training the VAE and applying DTW, we align and compare mIF and H&E slides. Our method improves differential diagnosis and therapeutic decisions by integrating morphological H&E data with immunophenotyping from mIF.
  arXiv  Detail & Related papers  (2023-06-11T21:30:20Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes [27.331511924585023]
  This work presents a novel multi-scale coordinated framework for learning multiple representations from multimodal neuroimaging data. We propose a general taxonomy of informative inductive biases to capture unique and joint information in multimodal self-supervised fusion. We show that self-supervised models reveal disorder-relevant brain regions and multimodal links without access to the labels during pre-training.
  arXiv  Detail & Related papers  (2022-09-07T01:37:19Z)
• rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes [71.1144397510333]
  We learned machine learning models to predict SNPs using 56 brain imaging QTs. SNPs within the known Alzheimer disease (AD) risk gene APOE had lowest RMSE for lasso and random forest. Random forests identified additional SNPs that were not prioritized by the linear models but are known to be associated with brain-related disorders.
  arXiv  Detail & Related papers  (2022-03-31T20:15:22Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Interpretable multimodal fusion networks reveal mechanisms of brain cognition [26.954460880062506]
  We develop an interpretable multimodal fusion model, gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCL's performed well for both classification and mechanism analysis.
  arXiv  Detail & Related papers  (2020-06-16T18:52:50Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Towards Patient Record Summarization Through Joint Phenotype Learning in HIV Patients [1.598617270887469]
  We propose an unsupervised phenotyping approach that jointly learns a large number of phenotypes/problems across structured and unstructured data. We ground our experiments in phenotyping patients from an HIV clinic in a large urban care institution. We find that the learned phenotypes and their relatedness are clinically valid when assessed by clinical experts.
  arXiv  Detail & Related papers  (2020-03-09T15:41:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.