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Updated: May 14, 2025

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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GAMMNet: Gating Multi-head Attention in a Multi-modal Deep Network for Sound Based Respiratory Disease Detection.

Shaokang Liu, Zhaoji Dai, Zihong Zhuang

    IEEE Journal of Biomedical and Health Informatics
    |May 12, 2025
    PubMed
    Summary

    A new deep learning model, GAMMNet, uses multi-modal respiratory sounds for accurate, contactless disease detection. This approach enhances early diagnosis and monitoring of respiratory conditions.

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    Area of Science:

    • Medical Technology
    • Artificial Intelligence
    • Respiratory Medicine

    Background:

    • Respiratory diseases pose significant global health challenges, with high morbidity and mortality.
    • Traditional diagnostic methods are costly, resource-intensive, and carry risks of cross-contamination.
    • Contactless sensing and deep learning offer promising alternatives for early respiratory disease detection.

    Purpose of the Study:

    • To introduce GAMMNet, a novel multi-modal neural network for enhanced respiratory disease detection.
    • To address the limitations in integrating multi-modal features for improved diagnostic accuracy.
    • To leverage contactless sound data for early detection and monitoring.

    Main Methods:

    • Development of GAMMNet, a multi-modal neural network utilizing contactless sound recordings.
    • Implementation of a unique gating mechanism to adaptively regulate modality influence.
    • Incorporation of multi-head attention and linear transformation modules for performance enhancement.

    Main Results:

    • GAMMNet achieved state-of-the-art classification results on real-world multi-modal respiratory sound datasets.
    • Demonstrated superior performance compared to existing deep learning methods.
    • Validated the model's effectiveness in contactless monitoring and early detection.

    Conclusions:

    • GAMMNet effectively enhances the detection of respiratory diseases using multi-modal sound data.
    • The model's gating mechanism and attention modules contribute to its robustness and accuracy.
    • This approach shows significant potential for non-invasive, early-stage respiratory disease diagnosis and management.