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A deep-learning AI tool significantly improved radiologist performance in diagnosing thoracic anomalies on chest radiographs (CXRs). The AI-assisted reading enhanced diagnostic accuracy and reduced reading time, showcasing its potential in clinical practice.

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Area of Science:

  • Radiology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Accurate diagnosis of thoracic anomalies on chest radiographs (CXRs) is crucial for patient outcomes.
  • Deep learning-based artificial intelligence (AI) tools are emerging to assist in medical image interpretation.
  • Evaluating AI tool performance and its impact on clinician performance is essential for clinical integration.

Purpose of the Study:

  • To assess the diagnostic performance of readers using a deep-learning AI tool (Rayvolve) for thoracic anomalies on CXRs.
  • To evaluate the standalone performance of the Rayvolve AI tool in detecting thoracic pathologies.
  • To determine the impact of AI assistance on reader diagnostic accuracy and reading time.

Main Methods:

  • A retrospective, multicentric study involving two phases.
  • Phase 1: Nine readers reviewed 900 CXRs with and without AI assistance; ground truth established by radiologist consensus.
  • Phase 2: Standalone performance of Rayvolve evaluated on 1500 CXRs.

Main Results:

  • AI-assisted reading significantly increased the area under the curve (AUC) by 15.94% (0.88 vs. 0.759).
  • Reading time decreased by 35.81% with AI assistance.
  • Sensitivity and specificity improved significantly with AI assistance (11.44% and 2.95% increases, respectively).
  • Standalone AI performance: sensitivity 0.964, specificity 0.844, PPV 0.757, NPV 0.9798.

Conclusions:

  • The deep-learning AI tool (Rayvolve) significantly enhances reader performance in diagnosing thoracic anomalies on CXRs.
  • AI assistance leads to improved diagnostic accuracy (sensitivity and specificity) and increased reading efficiency.
  • The Rayvolve AI tool demonstrates strong standalone performance for detecting thoracic pathologies.