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Pulmonary Tuberculosis IV01:26

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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
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Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Tuberculosis Disease Severity Assessment Using Clinical Variables and Radiology Enabled by Artificial Intelligence.

Marwan Ghanem1, Ratnam Srivastava1, Yasha Ektefaie1

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

The Journal of Infectious Diseases
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

Percent of lung involved (PLI) on chest X-ray (CXR) best predicts tuberculosis (TB) treatment outcomes. Combining PLI with clinical data improves risk stratification, and automated PLI assessment via convolutional neural networks (CNNs) enhances scalability.

Keywords:
artificial intelligencepercent of lung involved in diseasetuberculosis outcome prediction

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Area of Science:

  • Radiology
  • Infectious Disease Epidemiology
  • Artificial Intelligence in Medicine

Background:

  • Chest X-ray (CXR) is crucial for assessing pulmonary tuberculosis (TB) severity and guiding treatment duration.
  • Optimal radiological metrics and their integration with clinical data for predicting TB treatment outcomes remain unclear.

Purpose of the Study:

  • To evaluate radiological metrics from CXR for predicting unfavorable TB treatment outcomes.
  • To determine the optimal combination of radiological and clinical variables for risk stratification.
  • To develop an automated method for measuring a key radiological metric using artificial intelligence.

Main Methods:

  • Logistic regression analysis of human-read and AI-generated CXR metrics in a real-world TB dataset (n=2809).
  • Assessment of standalone predictive accuracy for 10 radiological features.
  • Development and fine-tuning of convolutional neural networks (CNNs) to automate percent of lung involved (PLI) measurement from CXR images (n=5261).

Main Results:

  • Human-read PLI was the only CXR finding associated with outcome across drug resistance and HIV subgroups.
  • PLI demonstrated superior predictive accuracy compared to cavitation (AUC 0.654 vs 0.581) and outperformed commercial AI features.
  • Combining PLI with age, sex, and smear grade improved outcome prediction (ΔAUC 0.028).
  • A CNN ensemble achieved high accuracy (AUC 0.850) in predicting PLI >25%.

Conclusions:

  • Percent of lung involved (PLI) is a superior radiological marker for predicting TB treatment outcomes compared to cavitation.
  • Integrating PLI with clinical variables enhances risk stratification for TB patients.
  • Automated PLI measurement using CNNs offers a scalable and accurate approach for clinical application.