ArticleNeuroImage. Clinical2014
Prediction of stroke thrombolysis outcome using CT brain machine learning.
Article in NeuroImage. Clinical, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.Journal of neurology · 2024Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 2026Article
- Healthcare professionals' knowledge, attitudes, and practices towards predictive diagnosis of early neurological deterioration.Scientific reports · 2025Article
- Layer by Layer: Assessing AI Diagnostic Accuracy With Incremental Case Information in Neuroradiology.Cureus · 2025Article
- Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Strokeformer: A novel deep learning paradigm training transformer-based architecture for stroke prognosis prediction.PloS one · 2025Article
- GPT-based prediction of short-term survival following decompressive hemicraniectomy in malignant middle cerebral artery infarction.Frontiers in neurology · 2025Article
- Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases.Scientific reports · 2024Article
- An intelligent learning system based on electronic health records for unbiased stroke prediction.Scientific reports · 2024Article
- Machine learning applications in stroke medicine: advancements, challenges, and future prospectives.Neural regeneration research · 2024Review
- Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke.Frontiers in neurology · 2024Article
- Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs.JMIR neurotechnology · 2024Article
- A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.Biomedical engineering online · 2023Article
- Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging.Neurointervention · 2023Article
- Application and risk prediction of thrombolytic therapy in cardio-cerebrovascular diseases: a review.Thrombosis journal · 2023Article
- Prediction of response to thrombolysis in acute stroke using neural network analysis of CT perfusion imaging.European stroke journal · 2023Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis - a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626-0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1-5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.