A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.
Authors
Affiliations (9)
Affiliations (9)
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
- Department of Pediatrics, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA.
- Department of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA.
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA.
- Department of Pediatrics, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA. [email protected].
- Department of Neurology, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA. [email protected].
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA. [email protected].
- Department of Psychiatry, Carver College of Medicine, The University of Iowa, Iowa City, IA, USA. [email protected].
- Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA. [email protected].
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.