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Developing an Artificial Intelligence Model for Reading Chest X-rays: Protocol for a Prospective Validation Study.

Queralt Miró Catalina1,2, Aïna Fuster-Casanovas1,2, Jordi Solé-Casals3,4

  • 1Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Spain.

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This study validates an artificial intelligence (AI) algorithm, ChestEye, for reading chest x-rays. The AI tool aims to improve diagnostic accuracy and speed, supporting clinicians in interpreting these common radiographic tests.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Chest x-rays are the most frequent radiographic tests, yet interpretation can be complex and prone to discrepancies.
  • Artificial intelligence (AI) offers a potential solution for enhancing the speed and reliability of chest x-ray interpretation.
  • Oxipit's ChestEye is an AI-powered platform designed for automatic computer-aided diagnosis of chest x-rays, capable of detecting numerous pathologies.

Purpose of the Study:

  • To prospectively validate the ChestEye AI algorithm as a diagnostic aid for chest x-rays.
  • To evaluate the accuracy, sensitivity, and specificity of the AI algorithm for single and multiple pathology detection.
  • To assess the AI's utility in supporting clinical decision-making.

Main Methods:

  • A prospective validation study comparing ChestEye AI diagnoses with reference radiologist diagnoses.
  • Anonymized chest x-ray images were processed by the AI algorithm and a radiologist.
  • Comparison of AI and radiologist assessments to calculate AI performance metrics (precision, sensitivity, specificity, accuracy) using confusion matrices and One-vs-All methodology.

Main Results:

  • Data collection was ongoing, with over 450 x-rays collected by June 2022, aiming for 600 samples.
  • The study anticipates gathering sufficient evidence to support AI as a valuable tool for chest x-ray interpretation.
  • Highlights the need for AI tools due to a declining number of radiology professionals.

Conclusions:

  • Satisfactory validation results could lead to ChestEye's implementation as a clinical support tool.
  • Potential benefits include increased diagnostic accuracy, speed, patient safety, and efficiency in primary care.
  • The AI tool may also contribute to reducing costs associated with unnecessary diagnostic tests.