IDEAL approach to the evaluation of machine learning technology in epilepsy surgery: protocol for the MAST trial. Issue 1 (27th January 2022)

• Record Type: Journal Article
• Authors: Chari, Aswin; Adler, Sophie; Wagstyl, Konrad; Seunarine, Kiran; Marcus, Hani; Baldeweg, Torsten; Tisdall, Martin
• Is Part Of: BMJ surgery, interventions, & health technologies. Volume 4:Issue 1(2022)
• Publication Date: 2022-01-27
• Subjects: neurostimulation devices -- neurological devices -- implantable neurostimulators; Surgery -- Periodicals; Surgery -- Research -- Periodicals; Medical care -- Periodicals; Medical care -- Research -- Periodicals; 617.005
• DOI: 10.1136/bmjsit-2021-000109
• Languages: English
• ISSNs: 2631-4940

Abstract

Abstract : Epilepsy and epilepsy surgery lend themselves well to the application of machine learning (ML) and artificial intelligence (AI) technologies. This is evidenced by the plethora of tools developed for applications such as seizure detection and analysis of imaging and electrophysiological data. However, few of these tools have been directly used to guide patient management. In recent years, the Idea, Development, Exploration, Assessment, Long-Term Follow-Up (IDEAL) collaboration has formalised stages for the evaluation of surgical innovation and medical devices, and, in many ways, this pragmatic framework is also applicable to ML/AI technology, balancing innovation and safety. In this protocol paper, we outline the preclinical (IDEAL stage 0) evaluation and the protocol for a prospective (IDEAL stage 1/2a) study to evaluate the utility of an ML lesion detection algorithm designed to detect focal cortical dysplasia from structural MRI, as an adjunct in the planning of stereoelectroencephalography trajectories in children undergoing intracranial evaluation for drug-resistant epilepsy.