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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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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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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 Chest X-Ray Image Retrieval System Using Deep Learning Based Biomarker Predictions.

Bradley C Lowekamp1, Andrei Gabrielian1, Darrell E Hurt1

  • 1Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA.

Proceedings of Spie--The International Society for Optical Engineering
|April 15, 2024
PubMed
Summary
This summary is machine-generated.

Tuberculosis (TB) is a leading cause of death, with rising drug-resistant cases. A new Chest X-ray retrieval system aids precision medicine by finding similar cases and treatments for drug-resistant TB.

Keywords:
chest x-raycontent-based image retrievaldeep learningexplainable biomarkersprecision medicinetuberculosis

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Area of Science:

  • Medical Imaging
  • Infectious Diseases
  • Artificial Intelligence

Background:

  • Tuberculosis (TB) remains a significant global health threat, with increasing drug-resistant strains complicating treatment.
  • The World Health Organization reported TB as a leading cause of death, with 450,000 new drug-resistant cases in 2021.
  • Effective treatment for drug-resistant TB is challenging, necessitating innovative approaches for patient care.

Purpose of the Study:

  • To develop and evaluate a Chest X-ray (CXR) based image retrieval system for precision medicine in drug-resistant TB.
  • To enable the retrieval of similar patient cases and their treatment regimens using CXR images.
  • To support clinical decision-making by facilitating the inspection of outcomes from comparable patients.

Main Methods:

  • A Chest X-ray image retrieval system was developed using the NIAID TB Portals dataset.
  • Image similarity was determined using clinically relevant biomarkers: gender, age, BMI, and lung sextant involvement.
  • Biomarker prediction utilized DenseNet169 convolutional neural networks with transfer learning from the NIH Clinical Center CXR dataset.

Main Results:

  • The system achieved high accuracy in predicting biomarkers: Gender AUC 0.9854, Age MAE 4.03 years, BMI MAE 1.67 kg/m².
  • Mean absolute errors for predicting sextant involvement ranged from 7% to 12%, with higher variability in upper lung regions.
  • The developed retrieval system is accessible online for clinical use.

Conclusions:

  • The TB Portals CXR retrieval system effectively enables precision medicine for drug-resistant TB.
  • The system facilitates the identification of similar patient cases, aiding in the selection of appropriate treatment strategies.
  • This AI-driven approach offers a valuable tool for managing complex drug-resistant TB cases.