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Related Concept Videos

Arteries of the Lower Limbs01:24

Arteries of the Lower Limbs

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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
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Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm.

Aswin Chari1,2, Sophie Adler2, Konrad Wagstyl2,3

  • 1Department of Neurosurgery, Great Ormond Street Hospital, London, UK.

Developmental Medicine and Child Neurology
|August 10, 2023
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Summary

This study evaluated a machine learning algorithm for detecting focal cortical dysplasia (FCD) in children with epilepsy undergoing SEEG. The algorithm aided in identifying the seizure-onset zone (SOZ), but did not increase the proportion of patients with SOZ identification.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Drug-resistant epilepsy in children often requires precise localization of the seizure-onset zone (SOZ) for surgical planning.
  • Focal cortical dysplasia (FCD) is a common cause of pediatric epilepsy, and its accurate detection is crucial for successful surgical outcomes.
  • Stereoelectroencephalography (SEEG) is an invasive neurophysiological method used to precisely localize the SOZ when non-invasive methods are insufficient.

Purpose of the Study:

  • To prospectively evaluate a machine learning-based lesion detection algorithm for identifying FCD in pediatric patients undergoing SEEG.
  • To assess the algorithm's ability to aid in the identification and sampling of the SOZ during SEEG procedures.
  • To determine the clinical utility and safety of integrating this algorithm into the presurgical evaluation workflow for drug-resistant epilepsy.

Main Methods:

  • A prospective, single-arm, interventional study was conducted involving children with drug-resistant epilepsy.
  • Structural MRI data from patients undergoing SEEG were analyzed by an FCD lesion detection algorithm to identify potential seizure onset areas.
  • The study assessed whether additional SEEG electrode placements, guided by the algorithm, targeted the SOZ, with the primary outcome being the proportion of patients with additional electrode contacts within the SOZ.

Main Results:

  • Twenty pediatric patients (median age 12 years) were enrolled; one did not undergo SEEG.
  • Additional electrode contacts were found in the SOZ in 1 of 19 patients, and algorithm-identified clusters were within the SOZ in 3 of 19 patients (already implanted).
  • A total of 16 additional electrodes were implanted in nine patients without adverse events. The algorithm changed resection boundaries in 1 of 19 patients, who became seizure-free post-surgery.

Conclusions:

  • The study demonstrated early-stage prospective clinical validation of a machine learning algorithm for FCD detection to assist SOZ identification in pediatric SEEG.
  • The algorithm's collocation with the SOZ occurred in 4 out of 19 patients, and it influenced resection planning in one case.
  • While the algorithm did not increase the overall proportion of patients with SOZ identified, lessons learned emphasize the need for robust prospective evaluation before routine clinical adoption.