BrainDrugs - WP5: Mining structural MRI data in Epilepsy
We aim to develop transparent predictive computer-based models to aid in identifying structural MRI abnormalities in epileptic patients. This includes establishing a text-mining model to automatically label radiology reports, as well as comparing MRI features identified by our deep learning image-based model proposal with the outcomes of existing task-specific methods in epilepsy research.
Description
Epilepsy is the most common serious chronic neurological disorder, affecting individuals of all ages. Structural magnetic resonance imaging (MRI) plays a critical role as a first-line radiological procedure in assessing abnormalities associated with epilepsy. Drug-resistant epilepsy patients face an increased risk of injury and mortality. Subtle epileptic features can sometimes be missed by the initial reporting radiologist who may have less specialized experience in epilepsy. Thus, relying solely on radiology reports may be insufficient, as anomalies might be overlooked due to poor image quality or a lack of expertise in epilepsy neuroimaging. Therefore, improving MR image assessment in epilepsy is essential.
This project is part of the Lundbeck Foundation-funded BrainDrugs Center, a research alliance based at the Neurobiology Research Unit (NRU) - Rigshospitalet, led by Prof. Gitte M. Knudsen (NRU) which aims to improve precision medicine in the treatment of depressive disorder and epilepsy.
Participants
- Led by Assoc. Prof. Melanie Ganz and Prof. Gitte M. Knudsen (NRU)
- Llucia Coll Benejam, postdoctoral researcher (NRU)
- Alice Schiavone, research assistant (NRU, UCPH)
