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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Updated: Aug 30, 2025

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External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke.

Grant Mair1, Philip White2, Philip M Bath3

  • 1Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

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|September 2, 2022
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Summary

The e-ASPECTS software showed moderate agreement with experts in detecting stroke features on CT scans but overcalled positive results. Further trials are needed before implementing this artificial intelligence (AI) tool in clinical practice.

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Area of Science:

  • Neuroradiology
  • Artificial Intelligence in Medicine
  • Stroke Imaging Analysis

Background:

  • Computed tomography (CT) is crucial for acute stroke assessment.
  • Alberta Stroke Program Early CT Score (ASPECTS) quantifies ischemic stroke extent.
  • AI-driven decision-support tools aim to improve stroke diagnosis accuracy and speed.

Purpose of the Study:

  • To evaluate the performance of e-ASPECTS software in identifying stroke features on CT scans.
  • To compare the diagnostic accuracy of e-ASPECTS with masked expert interpretations.
  • To assess e-ASPECTS performance in a simulated "front door" hospital population.

Main Methods:

  • Retrospective analysis of CT scans from 9 stroke studies (4,100 patients).
  • Comparison of e-ASPECTS results against final diagnoses and expert ratings.
  • Testing diagnostic accuracy for ischemia, hyperattenuated arteries, and hemorrhage in a representative population.

Main Results:

  • e-ASPECTS results agreed within one point of expert ASPECTS in 69% of cases.
  • Diagnostic accuracy was 71% for ischemic features and 85% for hemorrhage in the representative population.
  • e-ASPECTS showed higher false positive rates for both ischemia (12% vs 2%) and hemorrhage (14% vs <1%) compared to experts.

Conclusions:

  • e-ASPECTS demonstrates moderate agreement with expert interpretation but tends to overcall stroke features.
  • The software's performance in a broader, simulated hospital population indicates a need for caution.
  • Prospective trials are necessary to validate the impact of AI stroke software on patient care before widespread adoption.