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Related Concept Videos

Pneumonia III: Complications and Assessment01:30

Pneumonia III: Complications and Assessment

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Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
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Related Experiment Video

Updated: Jan 13, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Predicting Short-Term Outcome of COVID-19 Pneumonia Using Deep Learning-Based Automatic Detection Algorithm Analysis

Chae Young Lim1, Yoon Ki Cha1, Kyeongman Jeon2,3

  • 1Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro Gangnam-gu, Seoul 06351, Republic of Korea.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning algorithms analyzing chest radiographs can predict short-term outcomes in COVID-19 pneumonia. Changes in parameters from the deep learning-based automatic detection algorithm (DLAD) show prognostic value for patient improvement or deterioration.

Keywords:
COVID-19Grad-CAMchest radiographycommercial AItime-dependent receiver operating characteristic curve

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Area of Science:

  • Radiology
  • Artificial Intelligence in Medicine
  • Infectious Diseases

Background:

  • COVID-19 pneumonia presents a significant clinical challenge requiring accurate outcome prediction.
  • Serial chest radiographs (CXRs) are crucial for monitoring disease progression.
  • Deep learning algorithms offer potential for automated analysis of medical imaging.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning-based automatic detection algorithm (DLAD) in predicting short-term clinical outcomes for COVID-19 pneumonia patients.
  • To assess the prognostic value of parameters derived from DLAD applied to serial CXRs.
  • To identify imaging biomarkers for predicting patient improvement or deterioration.

Main Methods:

  • Analysis of serial CXRs from 391 COVID-19 pneumonia patients.
  • Application of a DLAD to quantify consolidation probability and area using heatmap segmentation.
  • Calculation of weighted areas and change rates (Δ) in imaging parameters.
  • Development and evaluation of Cox proportional hazards regression models for daily outcome prediction.

Main Results:

  • Baseline probability and Δparameters (ΔProbability, ΔArea, ΔWeighted area) derived from DLAD were significant prognostic indicators.
  • A multivariate Cox model using baseline probability and ΔWeighted area achieved optimal predictive performance (C-index: 0.75).
  • Time-dependent AUROC values ranged from 0.74 to 0.78, indicating reliable daily prediction accuracy.

Conclusions:

  • DLAD parameters, particularly changes over time (Δparameters), can effectively predict short-term clinical outcomes in COVID-19 pneumonia.
  • This AI-driven approach using serial CXRs shows promise for early identification of patients at risk of deterioration.
  • The findings support the integration of DLAD into clinical workflows for enhanced patient management.