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Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.

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Healthcare (Basel, Switzerland)
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Summary
This summary is machine-generated.

Detecting non-biomedical foreign objects (NBFO) and biomedical foreign objects (BFO) in chest X-rays (CXRs) is crucial. This study reviews methods to accurately identify, classify, and manage these objects for improved diagnostic accuracy.

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AI-guided toolschest X-rayclinical significanceforeign objectspulmonary abnormality

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Computer-Aided Diagnosis

Background:

  • Chest X-rays (CXRs) can contain non-biomedical foreign objects (NBFO) like coins and buttons, and biomedical foreign objects (BFO) such as medical devices.
  • These foreign objects can impede accurate interpretation of CXRs, leading to misdiagnosis or overlooking genuine abnormalities.
  • NBFOs and BFOs can be misclassified as biological abnormalities by machine learning algorithms, resulting in false positives.

Purpose of the Study:

  • To review and discuss the clinical significance of detecting and classifying NBFOs and BFOs in CXRs.
  • To examine the application of computer-aided detection (CADe) and diagnosis (CADx) tools for managing foreign objects in CXRs.
  • To highlight the importance of accurately isolating and either removing or highlighting foreign objects for effective abnormality analysis.

Main Methods:

  • Comprehensive review of 29 peer-reviewed research reports and articles.
  • Analysis of clinical reports detailing the impact of foreign objects on CXR interpretation.
  • Examination of shallow learning and deep learning algorithms applied in CADe and CADx tools for foreign object detection.

Main Results:

  • Foreign objects, both NBFOs and BFOs, significantly complicate CXR interpretation.
  • Machine learning algorithms, if not specifically trained, can erroneously identify foreign objects as pathological findings.
  • Effective detection, localization, and classification of foreign objects are essential for reliable diagnostic outcomes.

Conclusions:

  • Accurate detection, classification, and handling of NBFOs and BFOs are vital for improving diagnostic accuracy in chest radiography.
  • Advanced computational tools, including deep learning, show promise in addressing the challenge of foreign objects in CXRs.
  • Further research and development in CADe/CADx systems are needed to robustly manage foreign objects and enhance clinical decision-making.