Development of a Deep Learning Clinical Decision Support System for Pediatric Epilepsy Syndrome Diagnosis

Background and Aims: Epilepsy syndromes in children present with diverse clinical and electroencephalographic patterns, often leading to delayed or inaccurate diagnoses. This study aimed to develop a deep learning-based Clinical Decision Support System (CDSS) to assist in the early and accurate classification of pediatric epilepsy syndromes using clinical and EEG data. Methods: A multicenter retrospective dataset comprising ۱,۰۵۰ pediatric cases aged ۱–۱۶ years was compiled from three neurology centers. The dataset included clinical features (age of onset, seizure type, developmental history), EEG recordings, and MRI findings. EEG signals were preprocessed through artifact removal, wavelet decomposition, and spectral feature extraction. A hybrid deep learning model combining a ۱D Convolutional Neural Network (CNN) for temporal feature detection and a Bidirectional Long Short-Term Memory (Bi-LSTM) network for sequence modeling was implemented. Data were split into training (۷۰%), validation (۱۵%), and test (۱۵%) sets. Model performance was evaluated using multi-class accuracy, F۱-score, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC). The CDSS interface provided probabilistic outputs and visualizations for clinician use. Results: The CNN–BiLSTM model achieved an overall accuracy of ۷۲.۶% and an F۱-score of ۰.۸۱ across five major pediatric epilepsy syndromes. The system demonstrated high sensitivity in identifying benign epilepsy with centrotemporal spikes (۷۵.۴%) and childhood absence epilepsy (۷۳.۸%). The CDSS significantly outperformed traditional rule-based classification and showed high agreement with expert neurologists’ diagnoses (kappa = ۰.۸۲). Conclusion: This deep learning-powered CDSS shows promise in supporting neurologists by improving the diagnostic precision of childhood epilepsy syndromes.

کلیدواژه ها:

Epilepsy ، Deep Learning ، Clinical Decision Support