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Updated: Jun 23, 2025
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis
Jonas Asgaard Bojsen1, Mohammad Talal Elhakim2, Ole Graumann3
1Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark. jabo@rsyd.dk.
Objectives:
This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies.
Methods:
PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies.
Results:
Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079.
Conclusion:
Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated.
Critical Relevance Statement:
This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials.
Key Points:
There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI.
Insights
Artificial intelligence (AI) can accurately detect ischaemic stroke using magnetic resonance imaging (MRI) scans. Further research is needed to validate AI
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Growing interest in AI for medical diagnostics.
- Limited understanding of AI's performance in MRI-based stroke assessment.
- Need for systematic evaluation of AI stroke detection tools.
Purpose of the Study:
- Assess AI's stroke detection performance in MRI.
- Identify reporting deficiencies in AI-related studies.
- Evaluate AI for both ischaemic and haemorrhagic stroke detection.
Main Methods:
- Systematic review and meta-analysis following PRISMA guidelines.
- Searched MEDLINE, Embase, Cochrane Central, and IEEE Xplore.
- Included studies using MRI in adults for stroke detection with AI.
- Assessed risk of bias using a modified QUADAS-2 tool and reporting quality with MI-CLAIM checklist.
Main Results:
- Included 33 studies; 15 had low risk of bias.
- Low-risk studies demonstrated better reporting quality.
- AI achieved 93% sensitivity and 93% specificity for ischaemic stroke detection.
- Limited evidence for AI's effectiveness in detecting haemorrhagic stroke.
Conclusions:
- AI technology shows high accuracy in detecting ischaemic stroke via MRI.
- Further validation is required for haemorrhagic stroke detection.
- Clinical utility of AI for stroke detection in MRI needs investigation.
