ReviewJournal of neurointerventional surgery2023
Machine learning and acute stroke imaging.
Review in Journal of neurointerventional surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06018545 (AI Assisted Reader Evaluation in Acute CT Head Interpretation), which is not on this map. Cited by 37 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
AI Assisted Reader Evaluation in Acute CT Head Interpretation
Who cites it
37 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2026Pooled it
- Radiomics and ischemic stroke research: bibliometric insights and visual trends (2004-2024).Frontiers in neurology · 2025Pooled it
- Thrombectomy with and without computed tomography perfusion imaging for large-vessel occlusion stroke in the extended time window: a meta-analysis of randomized clinical trials.Frontiers in neurology · 2023Pooled it
- Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke.Annals of medicine · 2026Review
- Predictive analysis in acute ischemic stroke imaging.Annals of medicine and surgery (2012) · 2026Review
- Generative Artificial Intelligence to Automate Cerebral Perfusion Mapping in Acute Ischemic Stroke from Non-contrast Head Computed Tomography Images: Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Article
- Deep learning for fast screening and localization of spinal dural arteriovenous fistulas to enhance clinical workflow.NPJ digital medicine · 2026Article
- Motion artifacts and image quality in stroke MRI: associated factors and impact on AI and human diagnostic accuracy.European radiology · 2026Article
- A machine learning-based online prediction model for recurrence risk in patients withFrontiers in cellular and infection microbiology · 2026Article
- Current state and advancements of imaging in acute ischemic stroke: a practical review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- An artificial intelligence (AI)-based approach to clinical trial recruitment: The impact of Viz RECRUIT on enrollment in the EMBOLISE trial.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators.BMC medicine · 2025Article
- Workload and clinical impact of MRI-based extension of reperfusion therapy time window in acute ischaemic stroke-a prospective single-centre study.GeroScience · 2025Article
- Cerebrovascular morphology: Insights into normal variations, aging effects and disease implications.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2025Review
- Development of a machine learning model to predict changes in neuroimaging profiles among acute ischemic stroke patients following delayed transfer for endovascular thrombectomy.Neuroradiology · 2025Article
- A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.Molecular genetics and genomics : MGG · 2025Article
- Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke.Frontiers in neurology · 2025Article
- Machine learning models based on location-radiomics enable the accurate prediction of early neurological function deterioration for acute stroke in elderly patients.Frontiers in aging neuroscience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundIn recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.
objectiveTo provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.
methodsIn this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. We then review the literature for relevant publications. Searches were run in November 2021 in Ovid Medline and PubMed. Inclusion criteria included studies in English reporting use of artificial intelligence (AI), machine learning, or similar techniques in the setting of, and in applications for, acute ischemic stroke or mechanical thrombectomy. Articles that included image-level data with meaningful results and sound ML approaches were included in this discussion.
resultsMany publications on acute stroke imaging, including detection of large vessel occlusion, detection and quantification of intracranial hemorrhage and detection of infarct core, have been published using ML methods. Imaging inputs have included non-contrast head CT, CT angiograph and MRI, with a range of performances. We discuss and review several of the most relevant publications.
conclusionsML in acute ischemic stroke imaging has already made tremendous headway. Additional applications and further integration with clinical care is inevitable. Thus, facility with these approaches is critical for the neurointerventional clinician.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.