23 Jun 2023 • In a recent article published in JAMA Neurology, researchers tested whether the artificial intelligence (AI) model, SCORE-AI, interprets routine clinical electroencephalograms (EEGs) with comparable accuracy as human experts.

In the present diagnostic accuracy study, researchers developed and trained a convolutional neural network model, SCORE-AI, using a large dataset of highly annotated EEGs recorded between 2014 and 2020. The study encompassed all patients aged more than three months.

Further, the team tested how well SCORE-AI distinguished an abnormal from a normal EEG recording and classified abnormal recordings into the four most clinically relevant categories, viz., epileptiform-focal, epileptiform-generalized, non-epileptiform-focal, and non-epileptiform-diffuse abnormalities. This tool performed a fully automated analysis with no human interaction involvement.

The study development data set had 30,493 EEG recordings annotated by 17 experts. With no human involvement, SCORE-AI attained human expert-level performance in interpreting routine clinical EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers, researchers conclude.

Source: JAMA Neurology | Read full story