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Artificial intelligence in lung scintigraphy.

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Artificial intelligence (AI) is revolutionizing lung scintigraphy, enhancing diagnosis for conditions like pulmonary embolism (PE) and chronic obstructive pulmonary disease (COPD). This review details AI

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Area of Science:

  • Nuclear Medicine
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Lung scintigraphy is crucial for pulmonary embolism (PE) diagnosis and lung function assessment.
  • The integration of artificial intelligence (AI) is transforming this nuclear medicine modality.
  • AI applications span the entire lung scintigraphy workflow, from acquisition to reporting.

Purpose of the Study:

  • To review the current and future applications of AI in lung scintigraphy.
  • To explore AI's role in diagnosing various pulmonary conditions, including PE, COPD, CTEPH, and pneumonia.
  • To discuss AI-driven image enhancement, segmentation, and synthesis in lung scintigraphy.

Main Methods:

  • Review of clinically available, published, and potential future AI applications in lung scintigraphy.
  • Analysis of AI's impact on image acquisition, reconstruction, interpretation, and reporting.
  • Emphasis on the importance of representative datasets and internal validation for AI models.

Main Results:

  • AI demonstrates significant potential in PE detection, segmentation, and classification.
  • Emerging AI applications show promise for diagnosing COPD, CTEPH, and pneumonia.
  • AI facilitates image enhancement, cross-modality synthesis, and automated lung lobe segmentation.

Conclusions:

  • AI integration offers a revitalization pathway for lung scintigraphy against competing modalities.
  • Careful consideration of datasets and validation is crucial for successful AI implementation.
  • Clinicians must remain vigilant to prevent medical errors despite AI advancements.