aceArtificial intelligence‐assisted categorization of interictal abnormalities in adult long‐term electroencephalographic monitoring: Comparable diagnostic accuracy with marked efficiency gain
Abstract Objective Artificial intelligence (AI) classifies routine electroencephalograms (EEGs) with expert‐level accuracy, but interpretation of long‐term EEG monitoring (LTM) remains reliant on time‐intensive visual review. We evaluated whether autoSCORE, a clinically approved implementation of SCORE‐AI integrated into an EEG reader, achieves expert‐level diagnostic accuracy for LTM interpretation and whether expert‐supervised use reduces review time versus conventional analysis. Methods A fixed‐and‐frozen AI…
Expert review of autoSCORE detections shortened long-term EEG interpretation from a median of 52.4 to 2.2 minutes, improved interrater agreement, and raised sensitivity over conventional reading.
The hybrid approach's higher accuracy over conventional interpretation was not significant after multiple-comparison correction, and the data came from one center with only 78 recordings.
Evidence
- Peer-reviewedEpilepsia2026-10-17
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Truvace Impact Record TRV-2026-1818, v1: “Artificial intelligence‐assisted categorization of interictal abnormalities in adult long‐term electroencephalographic monitoring: Comparable diagnostic accuracy with marked efficiency gain.” Truvace, 2026-10-18. /record/TRV-2026-1818 (accessed at citation time). sha256 f4ce967da07382e9…
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