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DynMultiDep: A Dynamic Multimodal Fusion and Multi-Scale Time Series Modeling Approach for Depression Detection.

Jincheng Li1, Menglin Zheng1, Jiongyi Yang1

  • 1School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.

Journal of Imaging
|January 27, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces DynMultiDep, a novel framework for dynamic multimodal depression detection. It effectively integrates global and local patterns, improving detection accuracy for mental health conditions.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Psychiatry

Background:

  • Depression is a major global public health concern.
  • Current multimodal depression detection methods struggle with integrating long-term and short-term data patterns and adapting to varying data complexities.
  • Existing static fusion strategies limit the dynamic adaptation to complementary and redundant information across different data modalities.

Purpose of the Study:

  • To propose a dynamic multimodal depression detection framework, DynMultiDep.
  • To address the limitations of insufficient integration of global patterns and local fluctuations in long-sequence modeling.
  • To overcome the challenge of static fusion strategies in multimodal depression detection.

Main Methods:

  • Developed DynMultiDep, a framework combining multi-scale temporal modeling and adaptive fusion.
Keywords:
dynamic multimodal fusiondynamic routing mechanismintelligent depression detectionmulti-scale time series modelingresource-aware optimization

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  • Introduced the Multi-scale Temporal Experts Module (MTEM) using Mamba and Transformers for long-term and short-term feature extraction.
  • Implemented the Dynamic Multimodal Fusion module (DynMM) for adaptive modality selection and cross-modal interaction.

    • Main Results:

    • DynMultiDep demonstrated superior performance compared to existing state-of-the-art methods.
    • The framework achieved enhanced detection accuracy on two large-scale depression datasets.
    • The dynamic fusion mechanism effectively adapted to input characteristics, optimizing information integration.

    Conclusions:

    • DynMultiDep offers a significant advancement in dynamic multimodal depression detection.
    • The proposed MTEM and DynMM modules effectively address key bottlenecks in current methods.
    • This framework holds promise for improving the accuracy and adaptability of AI-driven mental health diagnostics.