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Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.

Henil P Shah1, Agha Sah Naqvi2, Parth Rajput1

  • 1GMERS Medical College, Gujarat, India.

Narra J
|May 12, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in detecting ground-glass opacities (GGOs) on chest CT scans. Models like WOANet demonstrate high accuracy and specificity, aiding early lung disease diagnosis.

Keywords:
Ground glass opacityX-ray imagedeep neural networkhigh-resolution CT-scanpulmonary nodule

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Area of Science:

  • Radiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Ground-glass opacities (GGOs) are crucial indicators of various lung pathologies on chest computed tomography (CT) scans.
  • Early detection of GGOs is vital for managing conditions such as COVID-19, pneumonia, and lung cancer.
  • Artificial intelligence (AI) offers advanced capabilities for analyzing complex medical imaging data.

Purpose of the Study:

  • To systematically evaluate the performance of AI models in detecting GGO nodules on high-resolution CT scans.
  • To assess AI models using key diagnostic metrics including accuracy, sensitivity, specificity, F1 score, AUC, and precision.
  • To synthesize findings from recent research on AI-driven GGO detection in lung imaging.

Main Methods:

  • A comprehensive literature search was conducted across PubMed, Google Scholar, Scopus, and ScienceDirect for studies published between 2016 and 2024.
  • Included studies focused on deep learning algorithms applied to high-resolution CT scans for GGO detection.
  • Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, with data synthesized qualitatively.

Main Results:

  • Out of 5,247 records, 18 studies met the inclusion criteria for AI-assisted GGO detection.
  • DenseNet achieved a high accuracy of 99.48%, while WOANet reported 98.78% accuracy with 98.37% sensitivity and 99.19% specificity.
  • WOANet demonstrated a strong balance between high specificity and sensitivity in GGO detection.

Conclusions:

  • AI models demonstrate significant potential for the accurate detection of GGOs on chest CT scans.
  • Specific AI architectures, such as WOANet, show promising diagnostic performance.
  • Future research should explore hybrid AI models to enhance clinical applicability and diagnostic yield for GGO detection.