463-02-007 ISMRM Abstract

Automated Prediction of Domain-Specific NIHSS from MRI Using Machine Learning for Acute Stroke Assessment

Primary: Neuro - Stroke and Cerebrovascular Disorders

Secondary: Analysis Methods - Classification and Prediction

463-02-007 · Imaging Stroke Recovery: From Microstructure to Personalized Therapy · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row D

Keywords: Machine Learning/Artificial Intelligence Clinical Decision-Making Open Science Data integration Post-stroke functional prediction

Accepted

Wen Zhang¹, Shun Liu¹, Andreia V Faria ²

¹Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, United States of America

²Radiology, Johns Hopkins University, Baltimore, United States of America

Presenting Author: Andreia V Faria

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References

1. 1. Comer, A. R. et al. National Institutes of Health Stroke Scale (NIHSS) scoring inconsistencies between neurologists and emergency room nurses. Front. Neurol. 13, 1093392 (2023). DOI: 10.3389/fneur.2022.1093392. PMID: 36712449. [doi] [pmid]

2. 2. Liu, C.-F. et al. A large public dataset of annotated clinical MRIs of patients with acute stroke and linked metadata. Sci. Data 10, 458 (2023). DOI: 10.1038/s41597-023-02457-9. PMID: 37607929. For the dataset: https://doi.org/10.3886/ICPSR38464.v5 [doi] [pmid]

3. 3. Liu, C. F. & Faria, A. V. Acute-stroke Detection Segmentation (ADS). https://www.nitrc.org/projects/ads/. DOI 10.5281/zenodo.5579390 [doi]

4. 4. Liu, C.-F. et al. Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke. Commun. Medicine 1, 61 (2021). DOI: 10.1038/s43856-021-00062-8. PMID: 35602200. [doi] [pmid]

5. 5. Liu, C.-F. et al. Digital 3D brain MRI arterial territories atlas. Sci. Data 10, 74 (2023). DOI: 10.1038/s41597-022-01923-0. PMID: 36739282. [doi] [pmid]

6. 6. Hollmann, N. et al. Accurate predictions on small data with a tabular foundation model. Nature 637, 319-326 (2025). DOI: 10.1038/s41586-024-08328-6. PMID: 39780007 [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/463-02-007