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Computed Tomography01:10

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Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
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The Nucleosome Core Particle01:12

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Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm.

Santiago Ortega-Gutierrez1, Juan Vivanco-Suarez1, Aaron Rodriguez-Calienes1

  • 1Department of Neurology University of Iowa Hospitals and Clinics Iowa City IA.

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Summary
This summary is machine-generated.

A new deep learning algorithm accurately estimates infarct core volume on noncontrast CT scans for acute ischemic stroke patients with large vessel occlusions. This tool shows performance comparable to CT perfusion, potentially improving treatment eligibility and speed.

Keywords:
computed tomographyinfarctmachine learningradiographic imagingsoftwarestroke

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Stroke Neurology

Background:

  • Noncontrast computed tomography (NCCT) offers a simplified approach for selecting acute ischemic stroke patients with large vessel occlusions (LVOs) for endovascular therapy.
  • This approach can reduce reperfusion time and expand treatment eligibility.
  • Accurate estimation of infarct core volume (ICV) is crucial for optimal patient selection.

Purpose of the Study:

  • To develop, train, and internally validate a deep learning algorithm (aICV-NCCT) for estimating baseline ICV on NCCT in anterior circulation LVO patients.
  • To compare the predictive performance of aICV-NCCT against Alberta Stroke Program Early Computed Tomography Score-NCCT and ICV-CT perfusion.
  • To assess the estimation of final infarct volume using diffusion-weighted magnetic resonance imaging at 24- to 48-hour follow-up.

Main Methods:

  • A deep learning algorithm (aICV-NCCT) was trained using stroke activations with baseline NCCT and CT angiography.
  • Internal validation employed intraclass correlations and Intersection over Union.
  • An external set of 230 patients with LVO, treated with endovascular therapy, was used for performance comparison, including those with CT perfusion data.

Main Results:

  • The algorithm achieved a high correlation (intraclass correlation coefficient, 0.78) and acceptable Intersection over Union (0.24) on the internal validation set.
  • On the external set, aICV-NCCT demonstrated performance similar to CT perfusion (ICC, 0.50 vs. 0.54; P = 0.764) in predicting final infarct volume.
  • Comparison with Alberta Stroke Program Early Computed Tomography Score-NCCT showed comparable results (rs, -0.41; P = 0.436).

Conclusions:

  • A deep learning algorithm (aICV-NCCT) was developed and validated, showing performance equivalent to CT perfusion for estimating core volume in acute stroke imaging.
  • This algorithm holds significant potential for settings with limited access to advanced imaging technologies.
  • The findings support the use of NCCT-based deep learning for improved acute ischemic stroke patient selection.