AI in Stroke Imaging: Enhancing Detection, Prognosis, and Workflow Efficiency

Introduction
Stroke is one of the leading causes of death and disability worldwide; it represents a major global health issue. Most of the strokes that occur globally are ischemic strokes caused by blocked blood flow to the brain. Since “time is brain,” it becomes essential to quickly diagnose and treat strokes because ongoing ischemia causes the loss of millions of neurons every minute.
The initial treatment approach for acute stroke cases requires essential neuroimaging techniques. Non-contrast CT (NCCT) usually serves as the initial imaging technique to rule out hemorrhage and assess early signs of ischemic changes in patients. CT angiography (CTA) along with CT perfusion (CTP) provides additional insights into blood vessel blockages and tissue viability. Traditional interpretation depends strongly on human expertise and can be limited by inter-reader variability, image quality, and time constraints in emergency settings.
Artificial intelligence, including machine learning (ML) and deep learning (DL), provides clinicians with advanced tools to improve decision-making in terms of time and accuracy. In this blog, we will highlight three primary studies that demonstrate how AI is transforming stroke imaging. Each study will be broken down to teach the underlying methodology, key findings, and practical implications.
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1. Deep Learning–Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time
Study: https://pubmed.ncbi.nlm.nih.gov/39255988/

ASPECTS stands for The Alberta Stroke Program Early CT Score. It utilizes a 10-point scale to measure early ischemic changes in the middle cerebral artery (MCA) territory through NCCT. For each affected region, 1 point is subtracted from 10. A lower score implies more extensive brain injury. It’s widely used to select patients for thrombectomy.

Source: https://radiopaedia.org/articles/alberta-stroke-programme-early-ct-score-aspects?lang=us

Alberta Stroke Program Early Computed Tomography Score template on non-contrast CT with 10 regions distributed over the MCA territory in ganglionic and supraganglionic levels.

Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC5226934/figure/F2/

Study Purpose
This study aimed to assess if using the deep learning algorithm (CINA-ASPECTS) would enhance physician performance in both the accuracy and speed of ASPECTS scoring.
Study Methodology
● 200 stroke cases were collected from five clinical sites.
● Each NCCT was reviewed by 8 readers (4 typical users, like neurologists, and 4 expert neuroradiologists).
● Each scan was interpreted twice: once unaided and once with AI assistance.
● A washout period was used to prevent memory bias.
Results
● Increased Accuracy: AI helped readers to achieve higher regional accuracy scores by matching expert references more closely, resulting in an improvement from 72.4% to 76.5%.
● Improved ROC-AUC: Diagnostic performance measurements showed an improvement from 0.749 to 0.788.
● Faster Read Time: The average interpretation time decreased by 6%.
● Better Interobserver Agreement: Reliability between readers improved significantly with AI support.
Clinical Takeaway:
DL-based algorithms assist in standardization of stroke assessments, reduce variation between clinicians, accelerate treatment decisions, and ultimately improve patient outcomes.
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2. CT-Based Intrathrombus and Perithrombus Radiomics for Prediction of Prognosis after Endovascular Thrombectomy: A Retrospective Study across 2 Centers
Study: https://pubmed.ncbi.nlm.nih.gov/39366763/
What is radiomics?
Radiomics involves extracting large sets of quantitative features from medical images. These features include texture, shape, and intensity, which capture details not visible to the human eye. When paired with ML models, radiomics can predict clinical outcomes.

Study Purpose
To develop a radiomics-based model using CT to predict prognosis after endovascular thrombectomy (EVT) in stroke patients.
Study Methodology
● 336 patients from two hospitals were included.
● Regions of interest (ROIs) were segmented around the thrombus (clot) and surrounding tissue (perithrombus).
● 428 radiomics features were extracted using Python tools.
● Multiple ML classifiers were tested: logistic regression, SVM, random forest, …
Results
● Combined Region Model: Including both thrombus and perithrombus regions improved accuracy significantly (AUC = 0.87).
● Best-Performing Model: Logistic regression offered the most stable performance.
● Clinical Correlation: Higher prediction accuracy can help anticipate recovery and complications early on.
Clinical Takeaway:
This model empowers physicians with an objective prognosis tool, allowing better-informed consent and patient counseling. It can also help customize rehabilitation plans and follow-up intensity.
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3. A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke
Study: https://pubmed.ncbi.nlm.nih.gov/38871371/
What is the First-Pass Effect (FPE)?
FPE refers to achieving successful vessel recanalization with a single thrombectomy attempt. FPE is associated with better clinical outcomes and fewer complications.

Examples of CT images, preprocessing, and final regional input section.

Source: https://pubmed.ncbi.nlm.nih.gov/38871371/

Study Purpose
To develop a deep learning model that predicts the likelihood of FPE using pretreatment MR and CT images.
Study Methodology
● 326 stroke patients who underwent EVT were studied.
● DL models were trained using using multiple imaging techniquesو including non-contrast CT and CT angiography as well as MRI sequences like diffusion-weighted imaging, FLAIR, and ADC maps.
● A hybrid transformer network (MNT-DL) with attention modules was designed.
● No manual segmentation was required, saving time.
Results
● CT-Based Prediction: AUC of 0.8051 with good sensitivity and specificity.
● MRI-Based Prediction: AUC of 0.7967.
Clinical Takeaways:
Being able to predict FPE before the procedure helps interventionalists anticipate challenges, prepare additional tools, and optimize procedural strategy. It also supports patient selection in resource-constrained environments.
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Conclusion
Artificial intelligence is not here to replace clinicians- it’s here to empower them. These studies show that AI can:
● Reduce variability in stroke imaging interpretation.
● Provide early, individualized prognostic insights.
● Improve operational efficiency in emergency stroke care.
As AI tools become more validated and integrated into clinical workflows, radiologists and neurologists alike will need to stay informed. For trainees, understanding the principles of AI in radiology is essential as it’s foundational to the future of the field.
The more we learn, the better we care: AI is simply the next chapter.