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Related Concept Videos

Pneumothorax-II01:27

Pneumothorax-II

121
Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:
121

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Real-World Performance of Pneumothorax-Detecting Artificial Intelligence Algorithm and its Impact on Radiologist

Joshua G Hunter1, Kaustav Bera2, Neal Shah2

  • 1Case Western Reserve University School of Medicine, Cleveland, OH (J.G.H.).

Academic Radiology
|October 30, 2024
PubMed
Summary
This summary is machine-generated.

An AI tool for detecting pneumothorax (PTx) on chest X-rays (CXR) demonstrated high accuracy for clinically significant PTx. This AI integration significantly reduced radiologist reporting times, enabling faster patient care.

Keywords:
AccuracyArtificial intelligenceChest X-rayPneumothoraxTriage

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Artificial intelligence (AI) algorithms for urgent findings in radiology are advancing.
  • The real-world clinical impact of AI in radiology requires further scientific investigation.
  • This study focuses on an FDA-approved AI tool for pneumothorax (PTx) detection on inpatient chest X-rays (CXR).

Purpose of the Study:

  • To investigate the diagnostic accuracy of an AI tool for PTx detection.
  • To evaluate the impact of an AI-integrated system on radiologist report turnaround times.
  • To assess the real-world clinical utility of AI in a large academic medical center's radiology practice.

Main Methods:

  • Retrospective analysis of 27,397 inpatient CXRs (August 2020 - April 2021).
  • Comparison between AI-integrated system (12,728 CXRs) and non-integrated system (14,669 CXRs).
  • Receiver operator characteristic (ROC) analysis for diagnostic accuracy and Wilcoxon rank sum tests for reporting times.

Main Results:

  • AI tool achieved an Area Under the ROC Curve (AUC) of 0.78 (sensitivity 0.60, specificity 0.97).
  • For moderate/large PTx, AUC increased to 0.93 (sensitivity 0.89, specificity 0.96).
  • Median reporting time for PTx-confirmed CXRs reduced by 46% with AI integration (100 vs. 186 minutes, p < 0.001).

Conclusions:

  • Real-world AI deployment for PTx detection shows strong diagnostic accuracy for actionable findings.
  • AI integration substantially reduced radiologist reporting times, enabling quicker clinical response.
  • AI tools can enhance clinical workflow and patient care for critical conditions like PTx.