ArticleCommunications medicine2021
Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke.
Article in Communications medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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.
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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
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke.Annals of medicine · 2026Review
- Remote Ischemic Conditioning on Infarct Growth in Patients With Acute Ischemic Stroke: Subgroup Analysis From the RESIST Trial.Stroke (Hoboken, N.J.) · 2026Article
- Deciphering Crossed Cerebellar Diaschisis in Patients with Large-Vessel Occlusion Acute Ischemic Stroke using BOLD Cerebrovascular Reactivity.Cerebellum (London, England) · 2026Article
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026Review
- Automated Multi-Modal MRI Segmentation of Stroke Lesions and Corticospinal Tract Integrity for Functional Outcome Prediction.Tomography (Ann Arbor, Mich.) · 2026Article
- Plug and Play Labeling Strategies for Boosting Small Brain Lesion Segmentation.Pattern recognition letters · 2026Article
- Article
- Automated DWI-FLAIR mismatch assessment in stroke using DWI only.European stroke journal · 2026Article
- Right hemisphere atrophy rate associates with naming recovery after left hemisphere ischaemic stroke.Brain communications · 2026Article
- Article
- Hemispheric atrophy as a predictor for naming recovery following left hemisphere ischemic stroke.medRxiv : the preprint server for health sciences · 2025Article
- Automated DWI-FLAIR mismatch assessment in stroke using DWI only.European stroke journal · 2025Article
- DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.Nature communications · 2025Article
- SDS-Net: A Synchronized Dual-Stage Network for Predicting Patients Within 4.5-h Thrombolytic Treatment Window Using MRI.Journal of imaging informatics in medicine · 2025Article
- Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts.Journal of imaging informatics in medicine · 2025Article
- PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation.Medical image analysis · 2025Article
- Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.Scientific reports · 2025Article
- Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.Journal of imaging informatics in medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.
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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.