Detection of Epileptic Spasms Using Foundational AI and Smartphone Videos: A Novel Diagnostic Approach for a Rare Neurological Disorder
Miron, G.; Halimeh, M.; Tietze, S.; Holtkamp, M.; Meisel, C.
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Infantile epileptic spasm syndrome (IESS) is a severe neurological disorder characterized by epileptic spasms (ES). Timely diagnosis and treatment are crucial but often delayed due to symptom misidentification. Smartphone videos can aid in diagnosis, but availability of specialist review is limited. We fine-tuned a foundational video model for ES detection using social media videos, thus addressing this clinical need and the challenge of data scarcity in rare disorders. Our model, trained on 141 children with 991 seizures and 127 children without seizures, achieved high performance (area under the receiver-operating-curve (AUC) 0.96, 83% sensitivity, 95% specificity) including validation on external datasets from smartphone videos (93 children, 70 seizures, AUC 0.98, false alarm rate (FAR) 0.75%) and gold-standard video-EEG (22 children, 45 seizures, AUC 0.98, FAR 3.4%). This study demonstrates the potential of smartphone videos for AI-powered analysis as the basis for accelerated IESS diagnosis and novel strategy for diagnosis of rare disorders.
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