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

Abstract

AimTo 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.MethodThis 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).ResultsTwenty 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.InterpretationWe 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.

Enhancing reproducibility of machine learning‐based studies in clinical journals through model cards

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