A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction

Michal Brzus; Joseph Griffis; Cavan J Riley; Joel Bruss; Carrie Shea; Hans J Johnson; Aaron D Boes

doi:10.1038/s41746-026-02803-2

Back

A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction

Journal article

Open access

Peer reviewed

A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction

Michal Brzus, Joseph Griffis, Cavan J Riley, Joel Bruss, Carrie Shea, Hans J Johnson and Aaron D Boes

NPJ digital medicine, Vol.9(1), 646

05/27/2026

DOI: 10.1038/s41746-026-02803-2

PMID: 42204350

Files and links (1)

url

https://doi.org/10.1038/s41746-026-02803-2View

Published (Version of record) Open Access

Abstract

Accurately predicting long-term outcomes after stroke remains a key challenge in personalized medicine. Here, we present a neuroimaging platform that forecasts individualized cognitive outcomes in patients with ischemic stroke using deep learning-based lesion segmentation and location-/network-based features. This novel, fully automated system is capable of processing raw DICOM MRI data from heterogeneous scanners and generating text-based, personalized outcome information. To demonstrate this pipeline, we trained cognitive outcome-prediction models using a large lesion cohort (N = 604) and applied them to an independent stroke cohort (N = 153). Multiple cognitive outcome predictions achieved reasonable accuracy, with 96% concordance with manual methods. A report generated by a large language model provides interpretable, patient-specific prognoses within ~3 min. This demonstrates the potential for imaging-informed prognostication to inform stroke care and guide rehabilitation strategies.

Details

Title: Subtitle: A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction
Creators: Michal Brzus - University of Iowa
Joseph Griffis - University of Iowa
Cavan J Riley - University of Iowa
Joel Bruss - University of Iowa, Stead Family Department of Pediatrics
Carrie Shea - University of Iowa
Hans J Johnson - University of Iowa
Aaron D Boes - University of Iowa
Resource Type: Journal article
Publication Details: NPJ digital medicine, Vol.9(1), 646
DOI: 10.1038/s41746-026-02803-2
PMID: 42204350
NLM abbreviation: NPJ Digit Med
ISSN: 2398-6352
eISSN: 2398-6352
Publisher: Springer Nature
Grant note: 1 R01 NS114405-02 / NINDS NIH HHS
Language: English
Electronic publication date: 05/27/2026
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Neurology; Electrical and Computer Engineering; Psychiatry; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; The Iowa Institute for Biomedical Imaging; Neurology (Pediatrics); The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
Record Identifier: 9985166869602771

Metrics

11 Record Views