Summary: Researchers successfully utilized machine learning to identify hidden neurological warning signs in the brain’s baseline electrical rhythms, bypassing the need to capture active seizures for an epilepsy diagnosis. The research demonstrates that an advanced pattern-recognition algorithm can detect subtle electroencephalogram (EEG) abnormalities linked to genetic epilepsy with high accuracy. This computational framework builds a customized “dictionary” of waveforms to expose underlying brain changes, establishing a clear pathway toward early pediatric intervention and noninvasive precision medicine. Key Facts - The Diagnostic Window Bottleneck: Neurologists rely heavily on EEGs to diagnose epilepsy, but standard clinical sessions provide only a 20-minute snapshot of brain activity, making manual detection incredibly difficult if a seizure does not naturally occur during the recording. - Building a Waveform Dictionary: Rather than tracking overt seizures, the AI algorithm treats baseline EEG readings like an unfamiliar language, identifying frequently repeating electrical patterns and learning their structural meaning in context to spotlight anomalies that human reviewers miss. - The Seizure-Free Assay: To test the system, researchers gathered multi-day EEG recordings from a panel of more than 40 mice, some of which carried epilepsy-causing variations in the TSC1 gene. The algorithm analyzed baseline segments containing zero seizure activity. - High-Accuracy Genetic Detection: The machine-learning approach successfully distinguished between different genetic backgrounds and identified the presence of the TSC1 mutation with high accuracy across two out of three mouse strains purely from baseline brain waves. - Pediatric Clinical Phase: Supported by the Delaware Clinical and Translational Research ACCEL Program, the team is transitioning the method into the clinic to analyze shorter EEG recordings from children undergoing epilepsy evaluations at Nemours Children’s Health. - Mitigating Family Anxiety: Epilepsy seizures follow natural, unpredictable cycles; identifying early, objective biomarkers can eliminate the high cognitive toll and profound anxiety families experience while waiting for an