AI Medical Compendium Topic

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Seizures

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Clinical Value of ChatGPT for Epilepsy Presurgical Decision-Making: Systematic Evaluation of Seizure Semiology Interpretation.

Journal of medical Internet research
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...

EEG detection and recognition model for epilepsy based on dual attention mechanism.

Scientific reports
In the field of clinical neurology, automated detection of epileptic seizures based on electroencephalogram (EEG) signals has the potential to significantly accelerate the diagnosis of epilepsy. This rapid and accurate diagnosis enables doctors to pr...

Artificial intelligence (ChatGPT 4.0) vs. Human expertise for epileptic seizure and epilepsy diagnosis and classification in Adults: An exploratory study.

Epilepsy & behavior : E&B
AIMS: Artificial intelligence (AI) tools like ChatGPT hold promise for enhancing diagnostic accuracy and efficiency in clinical practice. This exploratory study evaluates ChatGPT's performance in diagnosing and classifying epileptic seizures, epileps...

Select for better learning: identifying high-quality training data for a multimodal cyclic transformer.

Journal of neural engineering
. Tonic-clonic seizures (TCSs), which present a significant risk for sudden unexpected death in epilepsy, require accurate detection to enable effective long-term monitoring. Previous studies have demonstrated the advantages of multimodal seizure det...

Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine.

Epilepsy & behavior : E&B
Artificial intelligence (AI) is revolutionizing epilepsy care by advancing seizure detection, enhancing diagnostic precision, and enabling personalized treatment. Machine learning and deep learning technologies improve seizure monitoring, automate EE...

FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.

Journal of neural engineering
. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in fea...

Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.

Revue neurologique
INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intelligence (AI) - has revolutionized the collection, storage, and analysis of medical data in neurology...

Machine Learning and Deep Learning in Detection of Neonatal Seizures: A Systematic Review.

Journal of evaluation in clinical practice
BACKGROUND: Neonatal seizures are one of the most prevalent clinical manifestations of neurological conditions, requiring urgent intervention and detection. Machine learning (ML) and Deep Learning (DL) is an emerging promising tool for detecting and ...

Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.

Scientific reports
Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-la...

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

Scientific reports
Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models hav...