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Related Experiment Video

Updated: Oct 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

Weixiang Chen1, Xiaoyu Han2, Jian Wang3

  • 1Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.

Computers in Biology and Medicine
|December 25, 2021
PubMed
Summary

A novel deep learning model, Deep Diagnostic Agent Forest (DDAF), accurately identifies pneumonia pathogens from CT scans. This AI tool aids in faster etiological diagnosis, outperforming human experts in pathogen recognition.

Keywords:
Deep learningImbalanced dataPathogens of pneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • Pneumonia remains a leading global cause of death despite widespread antibiotic use.
  • Severe pneumonia outbreaks pose significant threats to public health and economic stability.
  • Accurate pathogen recognition is crucial for effective treatment and containment of infectious pneumonia.

Purpose of the Study:

  • To develop and evaluate a deep learning model for pathogen recognition in pneumonia using CT volumes.
  • To address the challenges of multiclass classification with imbalanced data and variations in CT scans.

Main Methods:

  • A retrospective study included 2,353 patients with CT volumes infected by one of 12 known pathogens.
  • The Deep Diagnostic Agent Forest (DDAF) model was proposed for pathogen recognition based on CT data.
  • The model was evaluated on its ability to perform multi-way classification for pathogen identification.

Main Results:

  • DDAF achieved an AUC of 0.899 ± 0.004 for level-I (5 pathogen groups) and 0.851 ± 0.003 for level-II (12 pathogens) recognition.
  • The model outperformed the average performance of seven human readers in level-I recognition.
  • DDAF surpassed all human readers in level-II recognition, where human accuracy averaged only 7.71 ± 4.10%.

Conclusions:

  • Deep learning models can effectively identify pneumonia pathogens using CT scans alone.
  • This AI-driven approach has the potential to accelerate etiological diagnosis.
  • The DDAF model offers a promising tool for improving pneumonia management.