Clinical validation of autoSCORE: A human-in-the-loop AI tool for interpretation of long-term EEG recordings.
Publication/Presentation Date
10-1-2026
Abstract
OBJECTIVE: To validate autoSCORE, a convolutional neural network (SCORE-AI) implemented into a commercially available EEG reader, for analysis of long-term EEG (LT-EEG) recordings, and to evaluate it within the human-in-the-loop approach.
METHODS: autoSCORE automatically divides LT-EEG into 2-hour segments, labeling each as normal or as containing one or more of four abnormality categories: focal epileptiform, generalized epileptiform, focal non-epileptiform, or diffuse non-epileptiform. In abnormal segments, it also inserts markers defined by start/stop times and tagged by abnormality type. Within each category, markers can be ranked by decreasing probability. Eighty 2-hour LT-EEG segments (40 in-hospital, 40 ambulatory) from patients aged 0.9-82.3 years were analyzed by autoSCORE. The diagnostic reference standard was derived from independent conventional visual assessment by three human experts.
RESULTS: Accuracy computed across expert-expert and AI-expert pairings, evaluated at the level of the 2-hour segments, were comparable, except for detection of focal non-epilepiform abnormality, where AI outperformed experts for identification accuracy (p = 0.019). The positive predictive value of the automated abnormality markers was 0.446-0.823. In the human-in-the-loop workflow, inspecting 10 automated markers with the highest probability in each category enabled correct identification of all abnormal segments, corresponding to 1.5%-5.5% of the full 2-hour segment.
CONCLUSIONS: autoSCORE accurately screens LT-EEG recordings and may serve as an adjunct to human EEG interpretation to reduce the workload of human experts.
SIGNIFICANCE: autoSCORE is a validated tool that can increase the productivity and efficacy of human experts in assessment of LT-EEG to improve patient diagnosis and management strategies.
Volume
193
First Page
2112430
Last Page
2112430
ISSN
1872-8952
Published In/Presented At
Tveit, J., Aurlien, H., Husain, A. M., Tarazi, A., Diehl, B., Gallentine, W., Tatum, W. O., Hahn, C. D., Schulze, D. G., Terney, D., Schomer, D. L., Aanestad, E., Gardella, E., Westhall, E., Gonzalez-Giraldo, E., Catenaccio, E., Cox, F., Fahoum, F., Tankisi, H., Hjelland, I., … Beniczky, S. (2026). Clinical validation of autoSCORE: A human-in-the-loop AI tool for interpretation of long-term EEG recordings. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 193, 2112430. Advance online publication. https://doi.org/10.1016/j.clinph.2026.2112430
Disciplines
Medicine and Health Sciences | Pediatrics
PubMedID
42868974
Department(s)
Department of Pediatrics
Document Type
Article