SCORE-AI achieved diagnostic accuracy similar to human experts in EEG diagnosis: JAMA

Electroencephalography (EEG) is most reliable diagnostic tool as it provides essential information to aid diagnosis and classification of epilepsy, important for therapeutic decision-making however requires special expertise unavailable in many regions of the world.

Researchers aimed 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.

The diagnostic study published in JAMA Neurology, they found that an AI model (SCORE-AI) separated normal from abnormal recordings then classify abnormal recordings as epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, or nonepileptiform-diffuse with a diagnostic accuracy similar to that of human experts.

The multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. 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.