ArticleTheranostics2021
Machine learning identifies stroke features between species.
Article in Theranostics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004-2023).Frontiers in neurology · 2025Pooled it
- Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.Physical and engineering sciences in medicine · 2026Article
- Development of a machine learning algorithm model to predict intraoperative hypotension in elderly patients undergoing thoracic and abdominal surgeries.Open medicine (Warsaw, Poland) · 2026Article
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
- Cerebrovascular GMolecular imaging and biology · 2023Article
- The predictors of death within 1 year in acute ischemic stroke patients based on machine learning.Frontiers in neurology · 2023Article
- Repurposing the mucolytic agent ambroxol for treatment of sub-acute and chronic ischaemic stroke.Brain communications · 2023Article
- Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data.Frontiers in neurology · 2023Article
- Machine learning based analysis of stroke lesions on mouse tissue sections.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022Article
- Article
- Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.BioMed research international · 2022Review
- A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke.Frontiers in aging neuroscience · 2022Article
- Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma.Journal of translational medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification and localization of ischemic stroke (IS) lesions is routinely performed to confirm diagnosis, assess stroke severity, predict disability and plan rehabilitation strategies using magnetic resonance imaging (MRI). In basic research, stroke lesion segmentation is necessary to study complex peri-infarction tissue changes. Moreover, final stroke volume is a critical outcome evaluated in clinical and preclinical experiments to determine therapy or intervention success. Manual segmentations are performed but they require a specialized skill set, are prone to inter-observer variation, are not entirely objective and are often not supported by histology. The task is even more challenging when dealing with large multi-center datasets, multiple experimenters or large animal cohorts. On the other hand, current automatized segmentation approaches often lack histological validation, are not entirely user independent, are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation.
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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.