RMIT University
Browse

Deep learning for reliable detection of epileptogenic lesions

chapter
posted on 2024-11-01, 03:26 authored by Mangor Pedersen, Cornelia VerspoorCornelia Verspoor, David Abbott, Mark Jenkinson, Ben Sinclair, Meng Law, Graeme Jackson
Deep learning techniques are ideally suited to find patterns in high-dimensional and complex imaging data, which means that it has the potential to change medicine and healthcare. In this chapter, we will highlight how deep learning can improve the lives of people living with treatment-resistant focal epilepsy by identifying brain surgery targets in this burdensome neurological disease. We will discuss topics that should be investigated to ensure a successful clinical translation of deep learning, by reliably detecting epileptogenic lesions-that is, targets for brain surgery-in high-dimensional brain imaging data such as magnetic resonance imaging or positron emission tomography. As deep learning techniques perform well with imaging data, we envisage that deep learning can support the identification of epileptogenic lesions and may therefore aid the presurgical process in treatment-resistant focal epilepsy.

Funding

Centre for Research Excellence in Digital Health

National Health and Medical Research Council

Find out more...

History

Start page

163

End page

175

Total pages

13

Outlet

Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence

Editors

Anitha S Pillai, Bindu Menon

Publisher

Elsevier

Place published

Netherlands

Language

English

Copyright

© 2022 Elsevier Inc. All rights reserved.

Former Identifier

2006121748

Esploro creation date

2023-05-03

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC