UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm

Chari, Aswin; Adler, Sophie; Wagstyl, Konrad; Seunarine, Kiran; Tahir, M Zubair; Moeller, Friederike; Thornton, Rachel; ... Tisdall, Martin M; + view all (2023) Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm. Developmental Medicine & Child Neurology 10.1111/dmcn.15727. (In press). Green open access

[thumbnail of Develop Med Child Neuro - 2023 - Chari.pdf]
Preview
Text
Develop Med Child Neuro - 2023 - Chari.pdf - Published Version

Download (1MB) | Preview

Abstract

AIM: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD: This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS: Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION: We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms.

Type: Article
Title: Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/dmcn.15727
Publisher version: https://doi.org/10.1111/dmcn.15727
Language: English
Additional information: © 2023 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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 Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
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 Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
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/10175271
Downloads since deposit
22Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item