BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908939190272000 |
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| author | Qu, Taiping Zhang, Hongkai Zhang, Lantian Zhao, Can Zhang, Nan Wang, Hui Zhou, Zhen Zou, Mingye Bo, Kairui Zhao, Pengfei Jin, Xingxing Su, Zixian Jiang, Kun Liu, Huan Du, Yu Wang, Maozhou Yan, Ruifang Wang, Zhongyuan Huang, Tiejun Xu, Lei Zhang, Henggui |
| author_facet | Qu, Taiping Zhang, Hongkai Zhang, Lantian Zhao, Can Zhang, Nan Wang, Hui Zhou, Zhen Zou, Mingye Bo, Kairui Zhao, Pengfei Jin, Xingxing Su, Zixian Jiang, Kun Liu, Huan Du, Yu Wang, Maozhou Yan, Ruifang Wang, Zhongyuan Huang, Tiejun Xu, Lei Zhang, Henggui |
| contents | Cardiac magnetic resonance (CMR) is a cornerstone for diagnosing cardiovascular disease. However, it remains underutilized due to complex, time-consuming interpretation across multi-sequences, phases, quantitative measures that heavily reliant on specialized expertise. Here, we present BAAI Cardiac Agent, a multimodal intelligent system designed for end-to-end CMR interpretation. The agent integrates specialized cardiac expert models to perform automated segmentation of cardiac structures, functional quantification, tissue characterization and disease diagnosis, and generates structured clinical reports within a unified workflow. Evaluated on CMR datasets from two hospitals (2413 patients) spanning 7-types of major cardiovascular diseases, the agent achieved an area under the receiver-operating-characteristic curve exceeding 0.93 internally and 0.81 externally. In the task of estimating left ventricular function indices, the results generated by this system for core parameters such as ejection fraction, stroke volume, and left ventricular mass are highly consistent with clinical reports, with Pearson correlation coefficients all exceeding 0.90. The agent outperformed state-of-the-art models in segmentation and diagnostic tasks, and generated clinical reports showing high concordance with expert radiologists (six readers across three experience levels). By dynamically orchestrating expert models for coordinated multimodal analysis, this agent framework enables accurate, efficient CMR interpretation and highlights its potentials for complex clinical imaging workflows. Code is available at https://github.com/plantain-herb/Cardiac-Agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04078 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging Qu, Taiping Zhang, Hongkai Zhang, Lantian Zhao, Can Zhang, Nan Wang, Hui Zhou, Zhen Zou, Mingye Bo, Kairui Zhao, Pengfei Jin, Xingxing Su, Zixian Jiang, Kun Liu, Huan Du, Yu Wang, Maozhou Yan, Ruifang Wang, Zhongyuan Huang, Tiejun Xu, Lei Zhang, Henggui Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Cardiac magnetic resonance (CMR) is a cornerstone for diagnosing cardiovascular disease. However, it remains underutilized due to complex, time-consuming interpretation across multi-sequences, phases, quantitative measures that heavily reliant on specialized expertise. Here, we present BAAI Cardiac Agent, a multimodal intelligent system designed for end-to-end CMR interpretation. The agent integrates specialized cardiac expert models to perform automated segmentation of cardiac structures, functional quantification, tissue characterization and disease diagnosis, and generates structured clinical reports within a unified workflow. Evaluated on CMR datasets from two hospitals (2413 patients) spanning 7-types of major cardiovascular diseases, the agent achieved an area under the receiver-operating-characteristic curve exceeding 0.93 internally and 0.81 externally. In the task of estimating left ventricular function indices, the results generated by this system for core parameters such as ejection fraction, stroke volume, and left ventricular mass are highly consistent with clinical reports, with Pearson correlation coefficients all exceeding 0.90. The agent outperformed state-of-the-art models in segmentation and diagnostic tasks, and generated clinical reports showing high concordance with expert radiologists (six readers across three experience levels). By dynamically orchestrating expert models for coordinated multimodal analysis, this agent framework enables accurate, efficient CMR interpretation and highlights its potentials for complex clinical imaging workflows. Code is available at https://github.com/plantain-herb/Cardiac-Agent. |
| title | BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.04078 |