Deep learning in structural and functional lung image analysis.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Pulmonology

Background:

• Deep learning (DL) is increasingly influencing medical imaging, with significant applications in lung image analysis.
• Research has focused on DL for various lung diseases and structures.

Purpose of the Study:

• To provide an overview of DL theory for lung image analysis.
• To systematically review DL applications in lung image segmentation, reconstruction, registration, and synthesis.

Main Methods:

• Systematic literature review following PRISMA guidelines.
• Initial search identified 479 studies; 82 met eligibility criteria.

Main Results:

• Segmentation was the most common DL application in lung imaging (65.9%).
• DL demonstrated strong performance in segmenting lung structures, and potential in registration, reconstruction, and synthesis.
• Only 12.9% of studies utilized functional lung imaging, indicating a research gap.

Conclusions:

• DL shows significant potential for lung image analysis, especially in segmentation.
• Widespread clinical adoption requires addressing validation inconsistencies, generalizability, transparency, and interpretability.
• Further research into functional lung imaging modalities is encouraged.