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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
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Related Experiment Video

Updated: Jun 5, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.

Hanieh Alimiri Dehbaghi1, Karim Khoshgard1, Hamid Sharini2

  • 1Department of Medical Physics, University of Medical Sciences, Kermanshah, Iran.

Plos One
|December 9, 2024
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Summary

This study developed an artificial intelligence model using radiomics and machine learning to improve the accuracy of diagnosing pneumothorax from CT scans. The Gradient Boosting Machine model achieved 98.97% accuracy, aiding radiologists and enhancing patient care.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiomics

Background:

  • Diagnostic imaging complexity can lead to errors, especially in critical conditions like pneumothorax.
  • Accurate and timely diagnosis of pneumothorax is crucial for patient outcomes.
  • Current diagnostic methods may face challenges with increasing imaging complexity.

Purpose of the Study:

  • To develop and evaluate an intelligent model for enhanced pneumothorax detection in CT scans.
  • To leverage radiomics features and machine learning to improve diagnostic accuracy.
  • To mitigate diagnostic errors and accelerate image interpretation for better patient care.

Main Methods:

  • Utilized CT scan data from 175 patients with suspected pneumothorax.
  • Preprocessed images using Matlab and extracted radiomics features.
  • Implemented and evaluated Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBoost), and Light GBM models.

Main Results:

  • The Gradient Boosting Machine (GBM) model achieved the highest accuracy (98.97%) and precision (99.55%).
  • XGBoost model showed high accuracy at 98.29%.
  • All models demonstrated strong sensitivity, with LightGBM (LGBM) reaching 100%.

Conclusions:

  • Artificial intelligence models show significant potential in supporting radiologists for pneumothorax diagnosis.
  • These AI tools can help prioritize positive cases and expedite evaluations.
  • The developed models can ultimately lead to improved patient outcomes in pneumothorax management.