SOURCE-LINKED INTELLIGENCE

Traumatic Spinal Cord Injury: The Need to Classify Disease Severity

CORDIS · observation · Publication date unknown

ta across hospitals. The aim of this project is to propose a fully automatic and reproducible analysis tool that could be run by clinicians to improve the clinical management of tSCI patients. First, deep learning models for automatic spinal cord and lesion segmentation from MRI images will be developed to go beyond the currently used error-prone and time-consuming manual segmentations. The models will be trained on a multi institutional MRI dataset to be robust to MRI data heterogeneity across hospitals. Then, quantitative measures of the tSCI severity will be automatically computed from the segmented structures (i.e., spinal cord and lesions) and employed within the statistical model to predict tSCI severity. Finally, the developed methodology will be translated to the real-world healthcare system and tested on a prospectively acquired dataset of tSCI patients. Importantly, deep learning models, analysis pipeline, and statistical model will be seamlessly integrated into the current state-of-the-art ecosystem for spinal cord MRI data analysis and made publicly available to facilitat

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recordType

award

status

SIGNED

region

EU

value

269047.2

unit

EUR