Noise in the diagnosis of epilepsy by experts

To measure the relative levels of signal and noise in expert diagnosis of epilepsy.

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).

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.

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.