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Artificial intelligence (AI) successfully guided trained healthcare professionals in acquiring diagnostic-quality lung ultrasound (LUS) images. This AI-assisted approach can expand LUS accessibility, especially in underserved regions.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Point-of-Care Ultrasound

Background:

  • Lung ultrasound (LUS) is crucial for diagnosing dyspnea but requires specialized expertise.
  • Artificial intelligence (AI) has shown promise in guiding novice users for cardiac ultrasound image acquisition.
  • The potential of AI to enhance LUS image acquisition for broader clinical application warrants investigation.

Purpose of the Study:

  • To assess the efficacy of AI in enabling trained healthcare professionals (THCPs) to obtain diagnostic-quality LUS images.
  • To validate the performance of an AI-guided LUS system against expert-acquired images.

Main Methods:

  • A multicenter diagnostic validation study involved 176 participants with shortness of breath.
  • Trained healthcare professionals (THCPs) used AI software (Lung Guidance AI) for LUS image acquisition.
  • AI software utilized deep learning algorithms to guide image capture and B-line annotation following an 8-zone protocol.
  • Images were reviewed by a panel of masked LUS experts for diagnostic quality assessment.

Main Results:

  • 98.3% of LUS examinations acquired by THCPs with AI assistance were deemed of diagnostic quality.
  • No significant difference in diagnostic quality was observed between AI-assisted THCPs and expert sonographers.
  • The AI system automatically captured images of diagnostic quality using a standardized 8-zone LUS protocol.

Conclusions:

  • AI-assisted LUS acquisition by THCPs can achieve diagnostic quality comparable to expert sonographers.
  • This AI technology has the potential to significantly increase access to LUS diagnostics.
  • Implementation of AI guidance can help overcome personnel shortages in expert LUS interpretation, particularly in underserved areas.