Citations

Purpose: These consensus guidelines by the American Clinical Neurophysiology Society (ACNS) describe best practices for performing intraoperative Electrocorticography (ioECoG) using subdural or depth electrodes for adult and pediatric population. Methods: A group of ACNS members was convened to develop technical standards for performing ioECoG. PubMed searches were performed to identify pertinent peer-reviewed literature. Sections were assigned to individual authors based on expertise. Consensus was achieved during subsequent group discussions to develop evidence-based recommendations to the extent possible. Recommendations: Communication between the neurosurgical and the neurophysiology teams is essential in verifying and documenting the location of the contacts. Most authors recommend ioECoG recordings of at least 5 and up to 30 minutes in duration to allow for sufficient observation of interictal activity. The anesthesia should be adjusted to allow continuous EEG activity and to minimize the effect on the ioECoG recording during general anesthesia or awake surgery. The surgical procedure and technical report should separate ioECoG recordings to define the irritative zone from ioECoG findings during functional mapping. The neurophysiology physician’s physical presence in the operating room is required in the definition of the services. Conclusion: These consensus guidelines by the ACNS describe best practices for performing intraoperative ECoG based on published literature and expert consensus.

Importance Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed. Objective To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG–Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse. Design, Setting, and Participants In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded. Main Outcomes and Measures Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients’ habitual clinical episodes obtained during video-EEG recording. Results The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models ( P < .001) and similar to human experts. Conclusions and Relevance In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.