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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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Related Experiment Video

Updated: Jun 3, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Published on: September 25, 2019

Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.

Tae Jin Yun1,2,3, Jin Wook Choi4, Hoon Na5,6

  • 1Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.

Scientific Reports
|June 1, 2026
PubMed
Summary

An artificial intelligence algorithm improved the detection of acute ischemic stroke (AIS) on non-contrast computed tomography (NCCT) scans. AI assistance enhanced diagnostic accuracy, particularly for non-radiologist physicians, aiding timely stroke diagnosis.

Keywords:
AI-assisted diagnosisAcute ischemic strokeDeep learningNon-contrast brain CTSmall infarct

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Published on: April 13, 2013

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Acute ischemic stroke (AIS) diagnosis relies on accurate interpretation of non-contrast computed tomography (NCCT).
  • Deep learning algorithms show promise in medical image analysis.
  • Evaluating AI performance across diverse reader expertise is crucial for clinical integration.

Purpose of the Study:

  • To assess the diagnostic performance of a deep learning algorithm for detecting AIS on NCCT.
  • To compare AI-assisted versus unassisted interpretation by various medical professionals.
  • To determine the impact of AI on diagnostic accuracy across different reader subgroups.

Main Methods:

  • Retrospective, multi-reader, randomized study of 917 NCCT cases.
  • Nine readers (non-radiologists, radiologists, neuroradiologists) interpreted scans with and without AI assistance.
  • Performance metrics included area under the ROC curve, accuracy, sensitivity, and specificity.

Main Results:

  • The AI model achieved an AUC of 0.8144 in standalone analysis.
  • AI-assisted interpretation significantly improved overall diagnostic accuracy (75.63% vs. 72.03%, p < 0.001).
  • Non-radiologist physicians demonstrated the largest accuracy improvement with AI assistance (5.38% increase, p < 0.001).

Conclusions:

  • AI-assisted interpretation enhances the detection of AIS on NCCT.
  • AI improves diagnostic performance across varying infarct volumes and reader expertise.
  • Integrating AI into clinical workflows can lead to more timely and equitable AIS diagnosis.