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Related Experiment Video

Updated: Jun 2, 2026

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

Development and Validation of a CT-based Deep Transfer Learning Radiomic Model for Predicting Post-COVID-19 Pulmonary

Jie Wang1,2, Pei Huang1,2, Jian Li3

  • 1Jiangxi Medical College, Nanchang University, Nanchang, China.

Current Medical Imaging
|June 1, 2026
PubMed
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This summary is machine-generated.

A deep transfer learning radiomic (DLR) model effectively predicts pulmonary fibrosis risk after COVID-19 using automated CT analysis. This tool aids early intervention for patients at risk of developing lung fibrosis post-infection.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonology

Background:

  • COVID-19 can lead to long-term complications, including pulmonary fibrosis.
  • Early identification of patients at risk for post-COVID-19 pulmonary fibrosis is crucial for timely intervention.

Purpose of the Study:

  • To evaluate a deep transfer learning radiomic (DLR) model for predicting 12-month pulmonary fibrosis risk post-COVID-19.
  • To assess the DLR model's efficacy compared to traditional clinical and radiomic models.

Main Methods:

  • Retrospective analysis of 260 COVID-19 patients with chest CT and clinical data.
  • A ResNet-50-based DLR model was developed for automated lesion segmentation and feature extraction.
  • Performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA).
Keywords:
COVID-19CTDeep learningPulmonary fibrosisRadiomicRadiomics.

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Published on: December 19, 2020

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Last Updated: Jun 2, 2026

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Main Results:

  • Age and hospital stay were independent predictors of fibrosis.
  • The DLR model demonstrated strong predictive performance (AUC=0.868).
  • The DLR model showed comparable or superior performance to clinical and nomogram models.

Conclusions:

  • The DLR model, utilizing automated segmentation, is an effective tool for predicting post-COVID-19 pulmonary fibrosis.
  • This automated approach offers significant clinical utility for risk stratification and management.
  • The DLR model advances predictive capabilities in clinical radiology for post-COVID-19 complications.