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Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

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Published on: September 1, 2023

Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison

Masaki Naganuma¹, Atsushi Tachibana², Takuya Fuchigami²

¹Department of Neurology, Saiseikai Kumamoto Hospital, Chikami 5-3-1, Minami-ku, Kumamoto, Japan.

Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association

|April 20, 2021

Summary

This summary is machine-generated.

A new deep learning software for calculating the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) shows superior accuracy compared to human experts. This tool aids in determining stroke treatment suitability, even in early stages.

Keywords:

ASPECTS Deep learning Fully convolutional network Machine learning Mechanical thrombectomy Stroke Thrombolysis

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Published on: June 2, 2023

Area of Science:

Neurology
Radiology
Artificial Intelligence

Background:

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is crucial for evaluating stroke severity and guiding reperfusion therapy.
Accurate ASPECTS calculation is vital for timely and effective stroke management.

Purpose of the Study:

To validate a novel deep learning software for automated ASPECTS calculation using a 3D fully convolutional network-based brain hemisphere comparison algorithm (3D-BHCA).
To compare the performance of the 3D-BHCA model against stroke neurologists in ASPECTS assessment.

Main Methods:

Retrospective analysis of head non-contrast CT data from 151 patients (71 acute ischemic stroke, 80 non-stroke).
Comparison of ASPECTS results from the 3D-BHCA model and 5 stroke neurologists against ground truth using region-based and score-based analyses.

Main Results:

The 3D-BHCA model demonstrated superior sensitivity, specificity, and accuracy in region-based analysis compared to neurologists.
Score-based analysis showed the 3D-BHCA model outperformed neurologists in dichotomized ASPECTS > 5 analysis and intraclass correlation.
The deep learning model maintained high performance across different onset-to-CT times, including the earliest periods.

Conclusions:

The developed deep learning-based automated ASPECTS calculation software is superior or equivalent to stroke neurologists.
This AI tool offers a reliable method for ASPECTS assessment in both acute stroke and non-stroke patients.