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Related Experiment Video

Updated: Apr 24, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.

Jinzhao Qian1,2, Ankita Joshi1, Hailong Li1,3,4

  • 1Imaging Research Center, Cincinnati Children's Hospital Medical Center, USA.

Artificial Intelligence and Applications (Commerce, Calif.)
|April 23, 2026
PubMed
Summary

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This summary is machine-generated.

Deep learning effectively handles missing medical imaging data in multi-modal studies by inferring absent information. This review analyzes deep learning solutions, identifying key methods and challenges for robust data analysis.

Area of Science:

  • Medical data analysis
  • Artificial intelligence in healthcare
  • Multi-modal data fusion

Background:

  • Missing data modalities disrupt comprehensive analysis in multi-modal studies.
  • Deep learning offers solutions for inferring and integrating absent information.
  • Ensuring model robustness and statistical power is crucial for multi-modal research.

Purpose of the Study:

  • To systematically review deep learning solutions for missing imaging modalities in multi-modal medical data.
  • To identify and categorize prevalent deep learning methodologies.
  • To explore research gaps, challenges, datasets, and evaluation metrics.

Main Methods:

  • Systematic literature review of articles from PubMed, IEEE Xplore, and Scopus (January 2013 - May 2025).
Keywords:
deep learningimage synthesisknowledge transferlatent spacemedical image analysismissing modalities

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  • Identification and eligibility screening of 234 initially identified articles, resulting in 61 selected studies.
  • Categorization of methods into image synthesis (47%), knowledge transfer (20%), and latent feature space (33%).

    • Main Results:

    • Image synthesis is the most common approach (47%) for addressing missing modalities.
    • Knowledge transfer (20%) and latent feature space methods (33%) are also significant.
    • Identified popular public datasets, evaluation metrics, and discussed associated challenges.

    Conclusions:

    • Deep learning significantly enhances multi-modal medical data analysis by addressing missing modalities.
    • Further research is needed to overcome existing challenges and explore future directions.
    • The review provides a comprehensive overview of current deep learning strategies and resources.