Ripart, Mathilde;
(2025)
Automated Detection of MRI Pathology in Epilepsy.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Epilepsy affects one in 100 people, with one third of patients experiencing seizures that are not adequately controlled by medication. For patients whose seizures are caused by a structural brain lesion - such as focal cortical dysplasia (FCD) or hippocampal sclerosis (HS) - surgical removal of the lesion may offer a path to seizure freedom. A key step in the presurgical planning is localising these lesions on MRI scans. However, some lesions are subtle and evade visual detection, leading to delays, costly and invasive additional testing, and lower rates of post-surgical seizure freedom. Computational tools have the potential to aid the detection of epilepsy lesions on MRI scans. This thesis describes the development of automated AI-based tools to detect subtle epilepsy lesions on MRI scans, to improve presurgical planning and ultimately ease the burden on patients and families. The thesis aimed to: 1. Develop automated AI tools to help detect subtle epilepsy lesions on MRI scans 2. Tailor these tools for use in clinical settings, to offer a radiological adjunct for the presurgical planning of patients with drug-resistant focal epilepsy. 3. Release these tools and code as open-source resources, to facilitate widespread adoption and accelerate future research. The thesis introduces three distinct automated tools: - MELD FCD: a machine-learning model for the detection of focal cortical dysplasia (Chapters 2 & 3) - AID-HS: a machine-learning model for the automated and interpretable detection (AID) of hippocampal sclerosis (Chapter 4) - MELD FE: a deep-learning model for the detection of a wide range of lesions in focal epilepsy (FE) (Chapters 5 & 6). This thesis also details the collation of large neuroimaging datasets of patients with focal epilepsy, which were used for the training and evaluation of these AI models. Finally, the thesis showcases how these tools were used in research practice around the world and highlights their potential to improve presurgical planning of patients with drug-resistant epilepsy and subtle MRI lesions. The figure below offers a visual abstract of the thesis.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Automated Detection of MRI Pathology in Epilepsy |
| Language: | English |
| Additional information: | Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| Keywords: | MRI, Machine-learning, Epilepsy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10202948 |
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