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This study presents the development and implementation of an open-source artificial intelligence (OS-AI) tool for the automatic identification and quantification of fibrotic patterns in chest computed tomography (CT) scans. CT studies from adult patients with suspected or confirmed pulmonary fibrosis were processed using 3D Slicer software, applying anatomical and pathological segmentation based on Hounsfield units. The method included volumetric analysis of the affected lung tissue and longitudinal evaluation of disease progression in follow-up cases. This project aims to provide an accessible, reproducible, and clinically useful solution for the assessment of interstitial lung diseases (ILD), with special focus on its use in low-resource settings.

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Identification and Quantification of Fibrotic Patterns in Chest CT: Implementation of open-source artificial intelligence (OS-AI)

  • 会议论文

  • 发表日期：

    2025年

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  • P. Carvajal,
  • E. Zirulnik,
  • D. Fino,
  • F. Gonzales,
  • J. Cortez,
  • N. Quiroga,
  • G. Zucarelli,
  • D. Haissiner,
  • V. Herrera,
  • R. Isoardi

摘要

This study presents the development and implementation of an open-source artificial intelligence (OS-AI) tool for the automatic identification and quantification of fibrotic patterns in chest computed tomography (CT) scans. CT studies from adult patients with suspected or confirmed pulmonary fibrosis were processed using 3D Slicer software, applying anatomical and pathological segmentation based on Hounsfield units. The method included volumetric analysis of the affected lung tissue and longitudinal evaluation of disease progression in follow-up cases. This project aims to provide an accessible, reproducible, and clinically useful solution for the assessment of interstitial lung diseases (ILD), with special focus on its use in low-resource settings.