AIMC Topic: Drug Resistant Epilepsy
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Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.
BMJ open
Jun 10, 2025
INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often necessitating surgical intervention. Deep brain stimulation (DBS) of the thalamus has been introduc...
Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation.
Journal of medical Internet research
May 12, 2025
BACKGROUND: For patients with drug-resistant focal epilepsy, surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures. Accurate localization of the EZ is crucial and is typically achieved through comprehensive p...
Deep learning-based EEG source imaging is robust under varying electrode configurations.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Apr 16, 2025
OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conventional ESI methods, limiting their utility in clinical settings with only low-density EEG available....
Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.
Neuroscience bulletin
Jan 27, 2025
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its ...
Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jan 24, 2025
OBJECTIVES: To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.
Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modula...
Combining MRI radiomics and clinical features for early identification of drug-resistant epilepsy in people with newly diagnosed epilepsy.
Epilepsy & behavior : E&B
Nov 29, 2024
OBJECTIVE: To identify newly diagnosed patients with drug-resistant epilepsy (DRE) based on radiomics and clinical features.
Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Nov 19, 2024
INTRODUCTION: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the con...
Seizure Sources Can Be Imaged from Scalp EEG by Means of Biophysically Constrained Deep Neural Networks.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Oct 29, 2024
Seizure localization is important for managing drug-resistant focal epilepsy. Here, the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging seizure activities from electroencephalogram (EEG) recordings in drug-res...
Machine learning models for predicting treatment response in infantile epilepsies.
Epilepsy & behavior : E&B
Oct 10, 2024
UNLABELLED: Epilepsy stands as one of the prevalent and significant neurological disorders, representing a critical healthcare challenge. Recently, machine learning techniques have emerged as versatile tools across various healthcare domains, encompa...
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