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Artificial Intelligence and Machine Learning in Lung Cancer Screening.

Scott J Adams1, Peter Mikhael2, Jeremy Wohlwend2

  • 1Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

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PubMed
Summary

Artificial intelligence and machine learning (AI/ML) can enhance lung cancer screening by improving eligibility assessment, image quality, nodule detection, and classification. These tools also offer opportunities for opportunistic screening via chronic disease assessment.

Keywords:
Artificial intelligenceLung cancer screeningLung nodule detectionMachine learningOpportunistic screeningRisk prediction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer remains a leading cause of cancer death, necessitating improved screening strategies.
  • Current lung cancer screening methods face challenges in efficiency, accuracy, and equitable access.
  • Artificial intelligence and machine learning (AI/ML) are emerging technologies with the potential to revolutionize medical diagnostics.

Purpose of the Study:

  • To review the current applications and future potential of AI/ML tools in lung cancer screening.
  • To highlight how AI/ML can address existing challenges in the lung cancer screening workflow.
  • To explore the role of AI/ML in improving health equity in lung cancer detection.

Main Methods:

  • Review of recent advancements in AI/ML algorithms applied to lung cancer screening.
  • Analysis of AI/ML applications across the entire screening pathway, from eligibility to follow-up.
  • Discussion of AI/ML's capability in image processing and data analysis for low-dose chest CT scans.

Main Results:

  • AI/ML tools show promise in determining screening eligibility and optimizing screening intervals.
  • AI/ML can significantly improve image quality through radiation dose reduction and denoising in low-dose CT.
  • AI/ML demonstrates effectiveness in lung nodule detection and classification, aiding in accurate diagnosis.
  • AI/ML can identify chronic diseases on CT, enabling opportunistic lung cancer screening.

Conclusions:

  • AI/ML is poised to significantly enhance lung cancer screening efficiency, accuracy, and accessibility.
  • The integration of AI/ML into lung cancer screening workflows can lead to earlier detection and improved patient outcomes.
  • AI/ML offers a pathway to improve population health and reduce disparities through enhanced and opportunistic screening programs.