Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence.

ABSTRACT:

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

SUBMITTER: Tveit J 

PROVIDER: S-EPMC10282956 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature