Nascimento, Fábio A.

Abstract Objective To measure the relative levels of signal and noise in expert diagnosis of epilepsy. Methods Twenty multinational epileptologists independently reviewed 50 vignettes of adult and pediatric patients presenting with suspected seizure(s) on two separate occasions with a ≥30‐day washout period. Experts provided a diagnosis of epilepsy or non‐epilepsy based on clinical information and, if requested, routine EEG and neuroimaging data. Cases had an established clinical diagnosis of epilepsy or non‐epilepsy based on capture of habitual paroxysmal events on video‐EEG or long‐term clinical follow‐up. Experts' judgments were analyzed to decompose variability into different sources: signal (objective differences between cases), level noise (experts' bias toward over/under‐diagnosis), pattern noise (experts' idiosyncratic reactions to specific case features), and occasion noise (inconsistency across occasions). Results The probability of an expert making a different diagnosis for a given case on two different occasions was 16%. The probability of two different experts making a different diagnosis for the same case was 26%. Signal (case “difficulty”) accounted for 66–69% of total variation, with 31–34% attributable to noise. Level noise was the largest contributor in the absence of EEG/neuroimaging results (23%), while pattern noise dominated when test results were available (24%). Occasion noise contributed relatively little (1%) but was still sufficient to cause diagnostic reversals in 16–22% between occasions. Significance The degree of noise in expert diagnosis of epilepsy is substantial, stemming primarily from physicians' idiosyncratic interpretations of case features and variable dispositions toward over‐ or under‐diagnosis. Strategies to improve reliability are needed, including standardized data collection protocols and structured decision algorithms. For “difficult cases,” where expert reliability and accuracy are lowest, our findings support current clinical practice which favors early referral for video‐EEG monitoring over reliance on diagnostic anchoring. This diagnostic pathway may become more accessible with advances in EEG technology (e.g., wearable devices) and artificial intelligence.

Abstract The accurate interpretation of scalp EEG remains an instrumental diagnostic component of epilepsy care. Knowledge of what constitutes normal EEG findings, non‐epileptiform abnormalities, and epileptiform patterns—both ictal and interictal—is essential for appropriate patient management. The International League Against Epilepsy (ILAE) has developed an educational curriculum containing learning objectives necessary to accurately diagnose and manage people with epilepsy. In this Seminar in Epileptology, we address the learning objectives related to assessment and interpretation of EEG background activity, normal sleep patterns, provocation methods, interictal abnormalities, and ictal patterns, as well as a brief discussion on relevant advances in scalp EEG analysis and future perspectives on this topic.

Purpose: To understand the current state of epilepsy surgery education delivered to epilepsy fellows in the United States. Methods: An online survey focused on characteristics of epilepsy surgery education was distributed to all 93 epilepsy fellowship program directors listed on the ACGME website (accessed in May 2022). Programs were stratified per the number of fellows currently enrolled: 0 to 3 (group A) and ≥4 (group B). Results: Forty-one of 93 (44%) epilepsy fellowship programs were included in the study. The average number of resective surgeries, ablations, or corpus callosotomies per year was mostly 0 to 30 (54%) in group A and mostly >30 (80%) in group B ( P = 0.05). The average number of intracranial implantations per year was mostly 0 to 20 (58%) in group A and mostly >20 (80%) in group B ( P < 0.05). The average number of neurostimulation implantations per year was 15 (range 0–90; vagal nerve stimulation), 7 (range 0–25; responsive neurostimulation), and 4 (range 0–10; deep brain stimulation). In 78% of programs, fellows are not required to present a minimum number of epilepsy surgery cases in multidisciplinary conference before graduation. Roughly half of programs (51%) reported not using objective measures to assess fellow competency in epilepsy surgery. Conclusions: Our results suggest significant variability in epilepsy surgery volume and, consequently, fellow exposure to surgical cases and lack of standardized, objective measures in fellow teaching and assessment in the field of epilepsy surgery across programs in the United States. We advocate development of a core epilepsy surgery curriculum including minimum standards at the national level.

Purpose: Delivering optimal care to patients with seizures and epilepsy requires all EEGs to be interpreted accurately and reliably. This study investigated neurology professionals' opinions on the ideal standards for EEG in clinical care. Methods: We developed an anonymous e-survey targeting practicing and trainee neurologists focused on participants' demographics, clinical practice characteristics, and views on optimal EEG standards of care—including whether an EEG certification test is needed and whether postresidency/fellowship training in EEG/epilepsy is necessary for neurologists who interpret outpatient/routine EEGs in practice. The survey was hosted by the Neurology Clinical Practice—Practice Current, and it was distributed online through the American Academy of Neurology, American Epilepsy Society, American Clinical Neurophysiology Society, and International League Against Epilepsy, and through social media. Results: Two hundred eighty-three responses were included: 119 from EEG/epilepsy-trained neurologists, 83 from non-EEG/epilepsy-trained neurologists, 75 from trainees, and 6 from advanced care providers. Most participants (78%) agreed that “an objective certification test of ability to interpret EEGs is needed for all those who interpret EEGs in clinical practice.” Most participants (71%) believed that outpatient/routine EEGs may be read only by neurologists with EEG/epilepsy training; this opinion was more prevalent among EEG/epilepsy-trained (83%) versus non-EEG/epilepsy-trained neurologists (55%). Conclusions: Our neurology community should discuss the need to develop and implement a certification test of ability for all neurologists who wish to interpret EEGs in clinical practice. In addition, it is imperative to improve in-residency EEG education to ensure that neurology graduates achieve EEG competence before entering the workforce.

AbstractObjectiveWe published a list of “must‐know” routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter‐rater agreement (IRA) of these “must‐know” rEEG findings among international experts.MethodsA previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple‐choice questions predicated on the previously published “must‐know” rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de‐identified 10–20‐s epochs of EEG that were considered unequivocal examples by five EEG experts.ResultsThe examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain‐specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p = .035). Country‐specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of “must‐know” rEEG findings are suboptimal and heterogeneous.SignificanceWe recommend the development and implementation of pragmatic, accessible, country‐specific ways to measure and improve the expert accuracy and IRA.

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 &amp;lt; .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.

AbstractCorrectly diagnosing and classifying seizures and epilepsies is paramount to ensure the delivery of optimal care to patients with epilepsy. Focal seizures, defined as those that originate within networks limited to one hemisphere, are primarily subdivided into focal aware, focal impaired awareness, and focal to bilateral tonic–clonic seizures. Focal epilepsies account for most epilepsy cases both in children and adults. In children, focal epilepsies are typically subdivided in three groups: self‐limited focal epilepsy syndromes (e.g., self‐limited epilepsy with centrotemporal spikes), focal epilepsy of unknown cause but which do not meet criteria for a self‐limited focal epilepsy syndrome, and focal epilepsy of known cause (e.g., structural lesions—developmental or acquired). In adults, focal epilepsies are often acquired and may be caused by a structural lesion such as stroke, infection and traumatic brain injury, or brain tumors, vascular malformations, metabolic disorders, autoimmune, and/or genetic causes. In addition to seizure semiology, neuroimaging, neurophysiology, and neuropathology constitute the cornerstones of a diagnostic evaluation. Patients with focal epilepsy who become drug‐resistant should promptly undergo assessment in an epilepsy center. After excluding pseudo‐resistance, these patients should be considered for presurgical evaluation as a means to identify the location and extent of the epileptogenic zone and assess their candidacy for a surgical procedure. The goal of this seminar in epileptology is to summarize clinically relevant information concerning focal epilepsies. This contributes to the ILAE's mission to ensure that worldwide healthcare professionals, patients, and caregivers continue to have access to high‐quality educational resources concerning epilepsy.