Summary
Traumatic spinal cord injury (tSCI) markedly reduces patients’ quality of life and economically burdens health systems. Neurological examinations and clinical magnetic resonance imaging (MRI) scans are currently insufficient for the proper classification of the tSCI baseline level (i.e., severity). Although MRI scans are routinely employed in tSCI patients, the MRI potential is not fully utilised due to the complexity of the analysis and diversity of MRI data 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 facilitate open science and reproducibility across hospitals. The project will create the first step in the improvement of care and clinical management in millions of patients with tSCI worldwide. In the longer term, after demonstrating the clinical relevance of the proposed tools, we assume that advanced MRI-based methods will be adopted by the larger clinical community for more personalised care.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101107932 |
| Start date: | 01-05-2023 |
| End date: | 30-04-2026 |
| Total budget - Public funding: | - 269 047,00 Euro |
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Original description
Traumatic spinal cord injury (tSCI) markedly reduces patients’ quality of life and economically burdens health systems. Neurological examinations and clinical magnetic resonance imaging (MRI) scans are currently insufficient for the proper classification of the tSCI baseline level (i.e., severity). Although MRI scans are routinely employed in tSCI patients, the MRI potential is not fully utilised due to the complexity of the analysis and diversity of MRI data 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 facilitate open science and reproducibility across hospitals. The project will create the first step in the improvement of care and clinical management in millions of patients with tSCI worldwide. In the longer term, after demonstrating the clinical relevance of the proposed tools, we assume that advanced MRI-based methods will be adopted by the larger clinical community for more personalised care.Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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