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. 2024 Aug 27;13(17):5080.
doi: 10.3390/jcm13175080.

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features

Kyung Min Kim  1 , Bo Kyu Choi  1   2 , Woo-Seok Ha  1 , Soomi Cho  1 , Min Kyung Chu  1 , Kyoung Heo  1 , Won-Joo Kim  3
Affiliations

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features

Kyung Min Kim et al. J Clin Med. .

Abstract

Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

Keywords: artificial intelligence; juvenile myoclonic epilepsy; machine learning; magnetic resonance imaging; prognosis.

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Conflict of interest statement

All the authors have no conflicts to disclose.

Figures

Figure 1
Figure 1
Flow chart depicting the study subjects.
Figure 2
Figure 2
Receiver operating characteristic (ROC) curves for models using clinical, radiological, and combined variables to predict poor prognosis of JME.
Figure 3
Figure 3
SHAP value summary plot for the XGBoost model. SHAP, Shapley additive explanations; XGBoost, extreme gradient boosting.
See this image and copyright information in PMC

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