Advancing Stroke Diagnosis: A Comprehensive Review of Artificial Intelligence in Detecting Early Ischemic Changes on Noncontrast CT (NCCT)
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) aids in detecting early ischemic changes on noncontrast computed tomography scans for acute ischemic stroke. AI tools like deep learning can objectively quantify brain damage, improving diagnosis and patient selection for treatment.
Area Of Science
- Neurology
- Radiology
- Artificial Intelligence
Background
- Early Ischemic Changes (EIC) detection on noncontrast computed tomography (NCCT) is critical for acute ischemic stroke (AIS) reperfusion therapy.
- Identifying subtle EIC is challenging due to variability, reader expertise dependence, and interobserver disagreement.
- Objective quantification of early ischemic brain damage is needed, especially in resource-limited settings.
Purpose Of The Study
- To review current artificial intelligence (AI) applications for detecting EIC on NCCT images in AIS.
- To highlight AI's potential in automating objective assessment of ischemic damage and improving diagnostic consistency.
- To explore the role of AI in enhancing AIS management and patient outcomes.
Main Methods
- Review of current AI applications, focusing on machine learning (ML) and deep learning (DL) for EIC detection.
- Discussion of automated Alberta Stroke Program Early Computed Tomography Score (ASPECTS) calculation using AI for objective hypoattenuation quantification.
- Examination of deep learning (DL) based EIC detection approaches utilizing large-scale datasets.
Main Results
- AI enables fast, consistent, and accurate analysis of NCCT images for EIC detection.
- Automated ASPECTS calculation and DL-based detection show promise for objective ischemic damage assessment.
- AI tools can complement clinical expertise and streamline diagnostic workflows.
Conclusions
- AI, particularly ML and DL, offers significant potential to improve the detection and quantification of EIC in AIS.
- These AI innovations can enhance diagnostic objectivity, efficiency, and potentially patient outcomes.
- Further research and development are crucial for realizing AI's full transformative role in AIS management.
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