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Related Experiment Video

Updated: Mar 21, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
04:44

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

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Automatic assessment of lung involvement in systemic sclerosis using deep learning.

Matin Esnaashari1, Roya Arian2, Ali Hajihashemi3

  • 1Al-Zahra Research Institute, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Research in Medical Sciences : the Official Journal of Isfahan University of Medical Sciences
|March 20, 2026
PubMed
Summary

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Quantitative CT Pulmonary Angiography and Echo Cardiography Analysis for Enhanced Cardiovascular Assessment of Right Ventricular Failure in Pulmonary Hypertension.

International journal of general medicine·2026

Deep learning accurately quantifies lung involvement in Systemic Sclerosis (SSc) patients using CT scans. A U-net model precisely segments interstitial lung disease patterns like reticulation and ground-glass opacity (GGO), improving diagnostic precision.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonology

Background:

  • Systemic sclerosis (SSc) is a connective tissue disorder often causing interstitial lung disease (ILD).
  • Computed tomography (CT) scans are crucial for identifying ILD patterns like reticulation and ground-glass opacity (GGO) in SSc.
  • Deep learning (DL) offers potential to enhance diagnostic precision and reduce human error in SSc-ILD assessment.

Purpose of the Study:

  • To develop and validate a DL model for segmenting and quantifying ILD patterns in SSc patients.
  • To assess the accuracy of a U-net model in identifying reticulation and GGO on lung CT scans.
  • To enable automated measurement of lung involvement in SSc.

Main Methods:

  • A dataset of 2190 lung CT slices from 22 SSc patients was collected and annotated.
Keywords:
Deep learningground-glass opacityinterstitial lung diseaselung involvementreticulationsystemic sclerosis

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  • A U-net model was employed to segment reticulation and GGO patterns.
  • An automated algorithm was used to outline lung regions and quantify affected areas.

    • Main Results:

    • The U-net model achieved high performance in segmenting reticulation (Dice coefficient: 87.22%) and combined GGO/reticulation (Dice coefficient: 86.20%).
    • The automated algorithm accurately outlined lung regions, facilitating precise quantification of lung involvement.
    • The DL approach demonstrated effectiveness in measuring disease extent in SSc patients.

    Conclusions:

    • Deep learning, specifically the U-net model, shows significant promise for accurate segmentation and quantification of lung involvement in SSc.
    • This automated approach can aid radiologists in diagnosing and monitoring SSc-related ILD.
    • DL techniques enhance diagnostic precision and minimize human error in SSc-ILD assessment.