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Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning

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Abstract

Cardiovascular disease (CVDs) has become one of the leading causes of death, posing a significant threat to human life. The development of reliable Artificial Intelligence (AI) assisted diagnosis algorithms for cardiac sounds is of great significance for early detection and treatment of CVDs. However, there is scarce research in this field. Existing research mainly faces three major challenges: (1) They mainly limited to murmur classification and cannot achieve murmur grading, but attempting both classification and grading may lead to negative effects between different multi-tasks. (2) They mostly pay attention to unstructured cardiac sound modality and do not consider the structured demographic modality, as it is difficult to balance the influence of heterogeneous modalities. (3) Deep learning methods lack interpretability, which makes it challenging to apply them clinically. To tackle these challenges, we propose a method for cardiac murmur grading and cardiac risk analysis based on heterogeneous modality adaptive multi-task learning. Specifically, a Hierarchical Multi-Task learning-based cardiac murmur detection and grading method (HMT) is proposed to prevent negative interference between different tasks. In addition, a cardiac risk analysis method based on Heterogeneous Multi-modal feature impact Adaptation (HMA) is also proposed, which transforms unstructured modality into structured modality representation, and utilizes an adaptive mode weight learning mechanism to balance the impact between unstructured modality and structured modality, thus enhancing the performance of cardiac risk prediction. Finally, we propose a multi-task interpretability learning module that incorporates an important evaluation using random masks. This module utilizes SHAP graphs to visualize crucial murmur segments in cardiac sound and employs a multi-factor risk decoupling model based on nomograms. And then we gain insights into the cardiac disease risk in both pre-decoupled multi-modality and post-decoupled single-modality scenarios, thus providing a solid foundation for AI assisted cardiac murmur grading and risk analysis. Experimental results on a large real-world CirCor DigiScope PCG dataset demonstrate that the proposed method outperforms the state-of-the-art (SOTA) method in murmur detection, grading, and cardiac risk analysis, while also providing valuable diagnostic evidence.

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Acknowledgements

The authors gratefully acknowledge the financial supports by the National Natural Science Foundation of China under Grant 62202332, Grant 62102008, and Diversified Investment Foundation of Tianjin under Grant 21JCQNJC00980, Grant 21JCQNJC01510.

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Author notes

  1. Chenyang Xu and Xin Li have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, Tianjin University of Technology, Tianjin, China

    Chenyang Xu, Xinyue Zhang, Ruilin Wu, Yuxi Zhou & Yue Gu

  2. Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China

    Xin Li

  3. DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China

    Yuxi Zhou & Yong Zhang

  4. Department of Cardiology, Peking University People’s Hospital, Beijing, China

    Qinghao Zhao

  5. National Institute of Health Data Science, Peking University, Beijing, China

    Shenda Hong

  6. Institute of Medical Technology, Peking University, Beijing, China

    Shenda Hong

  7. HeartVoice Medical Technology, Hefei, China

    Shijia Geng

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  1. Chenyang Xu
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Xu, C., Li, X., Zhang, X. et al. Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning. Health Inf Sci Syst 12, 2 (2024). https://doi.org/10.1007/s13755-023-00249-4

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