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Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings.

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This study developed a deep learning bone suppression model to remove obscuring ribs and clavicles in chest X-rays (CXRs). Bone-suppressed CXRs significantly improved tuberculosis detection accuracy in artificial intelligence models.

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Radiology and Deep Learning Applications

Background:

  • Chest X-rays (CXRs) are crucial for diagnosing cardiopulmonary conditions but can be hindered by bony structures.
  • Obscuring bony structures like ribs and clavicles in CXRs can lead to diagnostic errors and misinterpretations.
  • Existing deep learning (DL) workflows may be impacted by these limitations, affecting the detection of subtle abnormalities.

Purpose of the Study:

  • To develop and evaluate a DL-based bone suppression model for frontal CXRs.
  • To improve the accuracy of radiological interpretations, particularly for detecting tuberculosis (TB) manifestations.
  • To assess the impact of bone-suppressed images on the performance of DL models for TB classification.

Main Methods:

  • Trained and optimized several DL bone suppression models using a combined loss function.
  • Evaluated model performance using metrics like MAE, PSNR, SSIM, and MS-SSIM in a cross-institutional setting.
  • Fine-tuned a pre-trained VGG-16 model on bone-suppressed and non-bone-suppressed TB CXR datasets (Shenzhen and Montgomery) for TB classification.

Main Results:

  • The best bone suppression model (ResNet-BS) achieved high performance (PSNR = 34.0678; MS-SSIM = 0.9828).
  • DL models trained on bone-suppressed CXRs demonstrated significantly higher accuracy (AUC: 0.9535-0.9635) compared to those trained on non-bone-suppressed CXRs (AUC: 0.8991-0.8567).
  • Bone suppression enhanced model sensitivity towards TB classification and improved the detection of TB-consistent findings.

Conclusions:

  • DL-based bone suppression effectively removes obscuring bony structures in CXRs.
  • Bone-suppressed CXRs significantly improve the performance of DL models in detecting tuberculosis.
  • This approach holds promise for reducing diagnostic errors and enhancing AI-assisted radiological interpretation.