ArticleNature communications2025
DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- Artificial Intelligence in Ischemic Stroke Lesion Segmentation: A Narrative Review of Deep Learning Methods, Clinical Utility, and Future Directions.Journal of imaging informatics in medicine · 2026Review
- Artificial Intelligence-Driven Segmentation of Three Oral Diseases: Enabling Precision Diagnosis and Decision Support.International dental journal · 2026Article
- A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.NPJ digital medicine · 2026Article
- Deep Learning-Based Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Imaging.Biomimetics (Basel, Switzerland) · 2026Article
- Article
- Association of Acute Ischemic Stroke Volume With Post-Stroke Epilepsy Development.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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Authors and funding
59 authors.
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Abstract
Diffusion-weighted MRI is critical for diagnosing and managing ischemic stroke, but variability in images and disease presentation limits the generalizability of AI algorithms. We present DeepISLES, a robust ensemble algorithm developed from top submissions to the 2022 Ischemic Stroke Lesion Segmentation challenge we organized. By combining the strengths of best-performing methods from leading research groups, DeepISLES achieves superior accuracy in detecting and segmenting ischemic lesions, generalizing well across diverse axes. Validation on a large external dataset (N = 1685) confirms its robustness, outperforming previous state-of-the-art models by 7.4% in Dice score and 12.6% in F1 score. It also excels at extracting clinical biomarkers and correlates strongly with clinical stroke scores, closely matching expert performance. Neuroradiologists prefer DeepISLES' segmentations over manual annotations in a Turing-like test. Our work demonstrates DeepISLES' clinical relevance and highlights the value of biomedical challenges in developing real-world, generalizable AI tools. DeepISLES is freely available at https://github.com/ezequieldlrosa/DeepIsles .
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Registered trials
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