Development and Validation of Clinico-Imaging Machine Learning and Deep Learning Models to Predict Responses to Initial Antiseizure Medications in Epilepsy Publisher



Nazemzadeh MR ; Chang RSK ; Barnard S ; Pardoe HR ; Kuzniecky R ; Mishra D ; Kamkar H ; Nhu D ; Metha D ; Thom D ; Chen Z ; Ge Z ; Obrien TJ ; Sinclair B Show All Authors
Source: Epilepsia Published:2025

Abstract

Objective: Antiseizure medications (ASMs) are the first-line treatment for epilepsy, yet they are ineffective in controlling seizures in about 40% of patients with unpredictable individual response to treatment. This study aimed to develop and validate artificial intelligence (AI) models using clinical and brain magnetic resonance imaging (MRI) data to predict responses to the first two ASMs in people with epilepsy. Methods: People with recently diagnosed epilepsy treated with ASM monotherapy at the Alfred Hospital, Melbourne, Australia formed the development cohort. We developed AI models employing various combinations of clinical features, prescribed ASMs, and brain multimodal MRI images/features to predict the probability of seizure freedom at 12 months while taking the first or second ASM monotherapy. Five-fold cross-internal validation was performed. External validation was conducted on a validation cohort comprising participants of the Human Epilepsy Project. Results: The development cohort included 154 individuals (36% female, 85% focal epilepsy), of whom 29% had received both the first and second ASM monotherapy. The validation cohort included 301 individuals (61% female, all focal epilepsy), of whom 33% had received both the first and second ASM monotherapy. A fusion deep learning (DL) model comprising an 18-layer 3D videoResNet (for multi-sequence MRI data), a transformer encoder (ASM regimens), and a dual linear neural network (for clinical characteristics) outperformed other models. It achieved an internal cross validation F1 score of 0.75 ± 0.05 (average ± 95% confidence interval), higher than other machine learning (ML) models and DL models with less complex architecture or integration of fewer imaging sequences. This DL model significantly outperformed the best ML model on validation cohort (p < 0.001). Significance: AI-based models incorporating brain MRI, clinical, and medication data can efficiently predict seizure freedom in recently diagnosed epilepsy. They may enhance treatment selection in epilepsy and offer a foundation for clinical decision support systems. Further validation in larger cohorts is warranted. © 2025 Elsevier B.V., All rights reserved.