Expert level of detection of interictal discharges with a deep neural network.

ABSTRACT:

Objective

Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of experts to assess its potential applicability.

Methods

First, we performed clinical validation on an internal data set. Seven experts reviewed all EEG studies. Performance agreement between experts and the network was compared at both the EEG and IED levels. All EEG recordings were also processed with Persyst. Subsequently, we performed external validation, with data from four centers, using a hybrid approach, where detections by the deep neural network were reviewed by an expert. In case of disagreement with the original report,

SUBMITTER: Tjepkema-Cloostermans MC 

PROVIDER: S-EPMC11742546 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature